Systems and methods for bi-static or multi-static holographic navigation

ABSTRACT

The application relates to bi-static or multi-static holographic navigation systems, including methods of localizing an emitter or receiver with high precision relative to the sea floor. The system and methods can be used with a fully active sonar or radar system using well synchronized transmitters and receivers. The system and methods can be used with a passive sonar or radar system localizing a transmitter or a receiver based on poorly timed received signals.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/352,535, filed on Nov. 15, 2016, which is a continuation of U.S.patent application Ser. No. 13/848,570, filed on Mar. 21, 2013 (now U.S.Pat. No. 9,529,082), which claims priority to and the benefit of U.S.Provisional Ser. No. 61/613,838, filed on Mar. 21, 2012 and entitled“Bi-Static or Multi-Static Holographic Navigation.” The specificationsof each of the foregoing applications are incorporated by referenceherein in their entirety.

FIELD OF THE INVENTION

The present disclosure relates generally to systems and methodsassociated with synthetic aperture sonar (SAS) technology. Moreparticularly, in various implementations, the present disclosure relatesto holographic navigation.

BACKGROUND

In most land-based applications, navigation is often aided by in-placeinfrastructure such as GPS, radio beacons or a priori maps. Navigationand mapping underwater is difficult because among other things,wide-coverage underwater GPS-equivalents do not exist and large portionsof the sea bed are still unexplored.

Current techniques for underwater navigation use publicly availablebathymetry maps. However, these maps are relatively coarse andunsuitable for precision navigation. Other sonar-based navigationsystems rely on positioning schemes that use the sonar data itself. Forexample, on-the-fly acoustic feature-based systems attempt to use sonarto detect naturally occurring landmarks. Other solutions to thenavigation problem include deploying low-cost transponders in unknownlocations thereby enabling range-based measurements between the vehicleand transponder beacon. However, these transponders are often deployedat locations that are at great distances from each other, and often onlypartially observable because of the range-only information. Thus, thesetechnologies are unsuitable for navigation across small vehicle paths.

It is often desirable to be able to navigate terrain (whether on land orunderwater) in a vehicle equipped appropriately with sensors that allowthe vehicle to navigate the terrain relative to a prior map of theterrain. Holographic navigation is a technique that allows for precisenavigation of a vehicle equipped with sonar sensor array(s) relative toa prior map. Quite often, these vehicles are autonomous underwatervehicles (AUVs) or unmanned aerial vehicles (UAVs).

A synthetic aperture sonar (or radar) array on such a vehicle generatesat least one image that can be compared to an image associated with themap of the terrain and may be processed by a computer system on thevehicle to navigate the vehicle in relation to the prior map of theterrain. For instance, SAS arrays enable coherent correlation betweensonar signals, whether generated by the synthetic aperture, or not.

Existing holographic navigation systems suffer from a plurality ofdeficiencies, including the amount of power consumed by, the size, andthe shape of these systems. In some existing systems, for example, theperformance of a holographic navigation algorithm degrades substantiallyas transmitter frequencies increase and wavelengths decrease.

Furthermore, accurately localizing a transmitter based on receivedemissions is a known problem. Direct path observations can be used withan array or antenna to get a bearing to an object. Multiple direct pathobservations can be used for triangulation while waveguide models can beused for ranging from a single location. However, these techniques areall limited in precision. Extremely long arrays (such as SOSUS) canfocus on the location of an emitter object with fairly high precisionprovided that it is in the near field, but are extremely expensive.Hence, there is not an existing method of passively localizing anemitter with extremely high precision using a low cost receiver.

Accordingly, there is a need for high precision and low cost navigationsystems, particularly for underwater applications.

SUMMARY

The devices, systems and methods of the present application addressthese and other deficiencies of existing holographic navigation systems.Holographic navigation is an inexpensive way of coherently localizing anactive, monostatic (or quasi-monostatic) sonar or radar relative to aprior map of terrain. Instead of using a physical aperture on thevehicle, the terrain acts as the aperture, yielding resolution that is afunction of the spotlight on the seafloor. Given that the spotlight sizetypically grows with range, the scattering from that spotlight can beused to localize a robot with range invariant precision.

The present application includes a system and method of localizing anemitter or receiver with high precision relative to the sea floor. Itcan be used as either a fully active system; using well synchronizedtransmitters and receivers or as a passive system; localizing atransmitter or a receiver based on poorly timed received signals.

Placing a low frequency projector onto a separate platform (other than areceive platform), such as, without limitation, a large diameterunmanned underwater vehicle, may allow, for example, a mine neutralizerto reduce its sonar to small number of receive elements, which may havea relatively low weight. This enables low frequency holographicnavigation on a very small platform and, ultimately, a more robust andflexible mine neutralization solution. Similarly, submarines prefer notto use active sonar as it may reveal their location, but they have veryhigh quality receivers. Hence, bi-static holographic navigation canallow submarines to localize themselves based on seafloor scattering ofa sonar signal originating from a known source (either fixed or moving).

Bi-static holographic navigation can also be used to localize atransmitter using a known receiver. Submarines and stealth aircraftoften possess sonars and radars with electronically scanned phasedarrays that steer energy into narrowly defined areas to reduce theprobability of intercept. Although their outbound beams are narrow, thescattering off the seafloor or ground is less directional. Bi-staticholographic navigation can detect and localize those target based onscattering. Similarly, ships and whales are difficult to accuratelytrack and localize. However, bi-static holographic navigation can beused to track them and reduce collisions.

Bi-static holographic navigation, in its simplest form, may include twoplatforms with accurate synchronized clocks, a known transmit signalwith known timing, position information about the transmitter, and aprior bi-static map. The receive system (either a single channel or anarray) receives the signal, forms an image using timing and geometryinformation, applies grazing angle compensation, correlates the imageagainst a prior image, converts the correlation result to a probabilitydensity function, and uses that probability density function to updateits estimate of the state (position). High Frequency (HF) holographicnavigation techniques can be applied to remove various forms of phaseerror.

In a more advanced form, the emitting system (submarine, aircraft, andthe like) can emit a known signal from an unknown position at an unknowntime that would be received at a known location at a known time. Thatsignal would be back-projected or beam-formed, correlated, and used toupdate a position estimate.

Ina more difficult instance, the signal being emitted by the submarineor aircraft would be unknown. In this case, if the direct arrival wereknown, it could be used to form an image. If the signal were unknown, itwould be necessary to learn the signal, use it to form an image, andlocalize the target.

These techniques can also be performed multi-statically; applyingequally to sonar and radar. Although back-scatter is more desirable,forward scatter can be used. Bi-static holographic navigation follows asimilar process to monostatic holographic navigation. First, it isnecessary to create a prior map. Ideally, this is a bi-static syntheticaperture image containing all possible frequencies at all possibleangles. In practice, this will likely be band limited and angle limited.The remaining process depends on whether the transmitter and receiverare synchronized, which system has a known position, and whether thesignal is known.

The system and methods herein have many applications. For instance,consider the case of localizing a vehicle based on transmissions from aknown source. This applies to scenarios such as mine neutralization andsubmarine navigation. Bi-static holographic navigation allows smallautonomous mine neutralizers to use low frequency signals. Low frequencyacoustic signals are more robust to sedimentation than high frequencysignals, but low frequency projectors can weigh more than neutralizationplatforms. Hence, by utilizing a separate transmitter platform havingsufficient size to support a low frequency transmitter system, smallerneutralization platforms including receivers can be employed thatnavigate with high precision.

In one aspect, a system for navigating an underwater terrain includes afirst platform. The first platform includes an acoustic transmitterarranged to transmit an acoustic signal toward a portion of theunderwater terrain, resulting in a scattering signal emanating from theportion of the terrain. The first platform includes a first clockarranged to maintain timing information. The first platform alsoincludes a memory arranged to store: i) position information associatedwith at least one of the acoustic transmitter and an acoustic receiverand ii) one or more settings associated with the acoustic signal. Thefirst platform further includes a processor arranged to control theacoustic transmitter

The system also includes a second platform having an acoustic receiverarranged to receive a portion of the scattering signal. The secondplatform includes second clock that is synchronized with the firstclock. The second platform also includes a memory arranged to store: i)position information associated with at least one of the acoustictransmitter and acoustic receiver, ii) the one or more settingsassociated with the acoustic signal, and iii) a monostatic or bi-staticmap of the underwater terrain where the map includes acoustic data,within a first frequency range, obtained from synthetic aperture sonarimaging of the underwater terrain. The second platform includes a secondprocessor arranged to: i) generate a real aperture image of the portionof the underwater terrain based on the received portion of thescattering signal within a second frequency range and the timinginformation where the second frequency range can at least partiallyoverlap with the first frequency range, ii) modify the real apertureimage by compensating for grazing angle errors to generate a grazingangle invariant real aperture image and correct for phase errors in thegrazing angle invariant real aperture image, iii) coherently correlatethe modified real aperture image with the map, iv) convert thecorrelation result into a probability density function, and v) estimatethe position of one of the first platform and the second platform basedon the probability density function.

In one configuration, the first and second frequency ranges include amaximum frequency less than or equal to about 100 kHz. In otherconfiguration, the first and second frequency ranges include a minimumfrequency greater than about 100 kHz. The map may include a monostaticor bi-static map. Over modest angles, a monostatic prior map can be asubstitute for a bistatic prior map. The first platform may besubstantially stationary while the second platform is moving. The firstplatform may be moving while the second platform is substantiallystationary. An acoustic signal may include one or more characteristicsor settings such as, without limitation, a noise sequence, linear chirp,encoding, power, frequency, phase, and the like.

In some implementations, generating a real aperture image includes aplurality of real aperture images where each of the plurality of realaperture images represents a subset of the portion of the underwaterterrain. The phase error in each of the plurality of real apertureimages may be substantially constant. The acoustic receiver may includea single channel receiver or an array of receivers At least one of thefirst and second platforms may include an autonomous underwater vehicle(AUV). The underwater terrain may include at least a portion of a seabed.

In another aspect, an underwater vehicle includes an acoustic receiverarranged to receive a portion of a scattering signal where thescattering signal emanates from a portion of an underwater terrain. Thescattering signal may result from an acoustic signal being directedtoward a portion of the underwater terrain by an acoustic transmitter ona transmitter vehicle. The vehicle also includes a first clock havingtiming information and being synchronized with a second clock located atthe transmitter vehicle. The vehicle further includes a memory arrangedto store: i) position information associated with at least one of thetransmitting vehicle and the underwater vehicle, ii) one or moresettings associated with the acoustic signal, and iii) a map (monostaticand/or bi-static) of the underwater terrain where the map includesacoustic data, within a first frequency range, obtained from syntheticaperture sonar imaging of the underwater terrain. The vehicle may alsoinclude a processor arranged to: i) generate a real aperture image ofthe portion of the underwater terrain based on the received portion ofthe scattering signal within a second frequency range and the timinginformation where the second frequency range at least partially overlapswith the first frequency range, ii) modify the real aperture image bycompensating for grazing angle errors to generate a grazing angleinvariant real aperture image and correcting for phase errors in thegrazing angle invariant real aperture image, iii) coherently correlatethe modified real aperture image with the map, iv) convert thecorrelation result into a probability density function, and v) estimatethe position of one of the underwater vehicle and the transmittervehicle based on the probability density function.

In some implementations, the first and second frequency ranges include amaximum frequency less than or equal to about 100 kHz. In otherimplementations, the first and second frequency ranges include a minimumfrequency greater than about 100 kHz. The transmitter vehicle may besubstantially stationary while the underwater vehicle is moving. Thetransmitter vehicle may be moving while the underwater vehicle issubstantially stationary. Both vehicles may be moving or stationary.

In yet another aspect, a transmitter vehicle that enables underwaternavigation by an underwater vehicle includes an acoustic transmitterarranged to transmit an acoustic signal toward a portion of anunderwater terrain. This results in a scattering signal emanating fromthe portion of the terrain and a portion of the scattering signal beingreceived by the underwater vehicle. The underwater vehicle uses theportion of the scattering signal to: i) generate a real aperture imageof the portion of the underwater terrain based on the received portionof the scattering signal within a second frequency range and the timinginformation where the second frequency range at least partiallyoverlapping with the first frequency range, ii) modify the real apertureimage by compensating for grazing angle errors to generate a grazingangle invariant real aperture image and correct for phase errors in thegrazing angle invariant real aperture image, iii) coherently correlatethe modified real aperture image with the map, iv) convert thecorrelation result into a probability density function, and v) estimatethe position of one of the underwater vehicle and the transmittervehicle based on the probability density function.

The transmitter vehicle also includes a first clock arranged to maintaintiming information that is synchronize with a second clock located atthe underwater vehicle. The transmitter vehicle further includes amemory arranged to store: i) position information associated with atleast one of the transmitting vehicle and the underwater vehicle and ii)one or more the settings associated with the acoustic signal. Thetransmitter also includes a processor arranged to control the acoustictransmitter.

The first and second frequency ranges may include a maximum frequencyless than or equal to about 100 kHz. The first and second frequencyranges may include a minimum frequency greater than about 100 kHz. Thetransmitter vehicle may be substantially stationary while the underwatervehicle is moving. The transmitter vehicle may be moving while theunderwater vehicle is substantially stationary. Both vehicles may bemoving or stationary.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, advantages, and illustrativeimplementations of the invention will now be described with reference todrawings in which like reference designations refer to the same partsthroughout the different views. These drawings are not necessarily toscale, emphasis instead being placed upon illustrating principles of theimplementations.

FIG. 1 is a block diagram depicting a sonar mapping and navigationsystem.

FIG. 2 is block diagram of an exemplary computer system for implementingat least a portion of the systems and methods described in the presentdisclosure.

FIG. 3 depicts a transducer array in a sonar system.

FIG. 4 depicts a transducer array in a synthetic aperture sonar (SAS)system.

FIGS. 5A-5B depict a process for navigating a terrain using an exemplaryhigh-frequency sonar navigation system.

FIGS. 6A-6B depicts a process for navigating a terrain using anexemplary high-frequency sonar navigation system.

FIG. 7 depicts a process for correcting range varying phase errors in ahigh-frequency sonar system.

FIG. 8 depicts a process for using a plurality of orthogonal signals ina synthetic aperture sonar (SAS) system to generate images.

FIGS. 9A and 9B depict a transducer array used in connection with animplementation of the process depicted in FIG. 8.

FIG. 10 depicts a process for transmitting pulses from a syntheticaperture sonar (SAS) system having multiple transmitters.

FIGS. 11A-C depict a transducer array used in connection with animplementation of the process depicted in FIG. 10.

FIG. 12 depicts a process for simultaneous localization and mapping(SLAM) using real aperture sonar images.

FIG. 13 shows a bi-static holographic system including a transmitter andreceivers operating in an underwater environment.

FIG. 14 is a flow diagram of a process for estimating a receiverposition where timing is known, transmitter position is known, theacoustic signal is known, but the receiver position is unknown.

FIG. 15 is a flow diagram of a process for estimating a receiverposition where timing is unknown, transmitter position is known, theacoustic signal is known and the receiver position is unknown.

FIG. 16 is a flow diagram of a process for estimating a receiverposition where timing is unknown, transmitter position is known, theacoustic signal is unknown and the receiver position is unknown.

FIG. 17 is a flow diagram of a process for estimating a transmitterposition where timing is known, transmitter position is unknown, theacoustic signal is known and the receiver position is known.

FIG. 18 is a flow diagram of a process for estimating a transmitterposition where timing is unknown, transmitter position is unknown, theacoustic signal is known and the receiver position is known.

FIG. 19 is a flow diagram of a process for estimating a transmitterposition where timing is unknown, transmitter position is unknown, theacoustic signal is unknown, but the receiver position is known.

DETAILED DESCRIPTION

To provide an overall understanding of the systems and methods describedherein, certain illustrative implementations will now be described,including systems and methods for mapping and navigating a terrain.However, it will be understood by one of ordinary skill in the art thatthe systems and methods described herein may be adapted and modified forother suitable applications and that such other additions andmodifications will not depart from the scope thereof.

The present application includes a system and method of localizing anemitter or receiver with high precision relative to the sea floor. Itcan be used as either a fully active system; using well synchronizedtransmitters and receivers or as a passive system; localizing atransmitter or a receiver based on poorly timed received signals.

Placing a low frequency projector onto a separate platform (other than areceive platform), such as, without limitation, a large diameterunmanned underwater vehicle, may allow, for example, a mine neutralizerto reduce its sonar to a small number of receive elements, which mayhave a relatively low weight. This enables low frequency holographicnavigation on a very small platform and, ultimately, a more robust andflexible mine neutralization solution. Similarly, submarines prefer notto use active sonar as it may reveal their location, but they have veryhigh quality receivers. Hence, bi-static holographic navigation canallow submarines to localize themselves based on seafloor scattering ofa sonar signal originating from a known source (either fixed or moving).

The systems and methods described herein also include high-frequency(“HF”) holographic navigation, namely map-based navigation using themulti-aspect holographic-nature of synthetic aperture sonar (SAS) imagescaptured at frequencies greater than or equal to about 100 kHz. Thesystems and methods described herein further include low frequency(“LF”) holographic navigation at frequencies less than about 100 kHz. Inparticular, the systems and methods described herein allow for coherentcorrelation between images, currently captured, and prior maps whenthere is an overlap in frequency and aspect. Such coherent correlationallows for position and/or heading-based navigation. At high-frequency,the inventor has recognized that images suffer from spatially varyingphase errors (e.g., range varying phase errors), which cause imageand/or correlation distortion. Such phase errors may exist even at lowfrequencies when there are altitude variations. In certainimplementations, when the phase errors are much smaller than thebandwidth, although images may not be distorted, correlation (andtherefore navigation) may become difficult. The systems and methodsdescribed herein overcome the deficiencies of the prior art byintroducing a phase error corrector configured to cut the image intosmaller regions where phase is relatively constant and use these phasemeasurements to correct portions of the image.

The systems and methods described here make use of various other aspectsof the holographic nature of synthetic aperture images, which theinventor has recognized. For example, systems and methods are describedherein for determining a three-dimensional model of a shape based on itstwo dimensional shading and shadowing of acoustic signals. The systemsand methods described herein include methods for positioning sensors(such as Tsunami sensors) and navigation beacons with high-precisionusing HF holographic navigation. The systems and methods describedherein include methods for monitoring and modeling a water column usingan autonomous underwater vehicle (AUV) based on high-precision locationmeasurements obtained using HF holographic navigation. In certainimplementations, the systems and methods include a seismic survey systemhaving a combination of orthogonal transmitters and multiple receiversto form a full planar synthetic aperture sonar with higher resolution.

In other aspects, the systems and methods described herein includeadding multiple transmitters to the array and generating orthogonalpinging sequences. In particular, the systems and methods describedherein include a SAS having a low-grating sidelobe, a SAS having a highcoverage rate using multiple transmitters, and an overpinging sequencefor increasing the range of the SAS system. The systems and methodsdescribed herein further include bistatic and monostatic holographicgapfilling techniques for localizing an emitter or receiver with highprecision relative to a terrain. In still other aspects, the systems andmethods described herein include simultaneous localization and mapping(SLAM) techniques that involve beamforming a real aperture image suchthat it can be coherently correlated with a prior real aperture image ofoverlapping frequencies. Each of these and other systems and methodsdescribed herein may be used independently of each other or in anysuitable combination of one or more any other system and method.Modifications and variations described with reference to a system andmethod described herein may be applied to any other system and methoddescribed herein, without departing from the scope of the presentdisclosure.

In the following passages, an illustrative mapping and navigation systemand an illustrative computer system for executing holographic navigationand mapping is described with reference to FIG. 1-4, respectively.Further illustrative implementations of components and processes of theholographic navigation and mapping system include processes fornavigating a terrain, for example an underwater terrain, using a map aredescribed with reference to FIGS. 5 and 6. To allow for high-frequencyholographic navigation, FIG. 7 describes a process for correcting rangevarying phase errors, recognized by the inventor to be a reason for thefailure of traditional holographic navigation, coherent correlation, andchange detection systems at higher frequencies. FIGS. 8-9B depict aprocess and components for generating SAS images having low gratingsidelobes, and FIG. 10-11B depict a process and components forgenerating a high-coverage rate SAS signals. Finally, FIG. 12 describesa holographic SLAM process for navigating a terrain.

FIG. 1 is a block diagram depicting a sonar mapping and navigationsystem 100. The system 100 includes a sonar unit 110 for sending andreceiving sonar signals, a preprocessor 120 for conditioning a received(or reflected) signal, and a matched filter 130 for performing pulsecompression and beamforming. The system 100 is configured to allow fornavigating using high-frequency (greater than about 100 kHz) sonarsignals. To allow for such HF navigation, the system 100 includes asignal corrector 140 for compensating for grazing angle error and forcorrecting phase error. The system 100 also includes a signal detector150 for coherently correlating a received image with a map. In certainimplementations, the system may be mounted on vehicle navigating over aterrain, such as an autonomous underwater vehicle (AUV) or an unmannedaerial vehicle (UAV). In such implementations, the system 100 includesan onboard navigation controller 170, motor controller 180 and sensorcontroller 190. The navigation controller 170 may be configured toreceive navigational parameters from a GPS/RF link 172 (when available),an accelerometer 174, a gyroscope, and a compass 176. The motorcontroller 180 may be configured to control a plurality of motors 182,184 and 186 for steering the vehicle. The sensor controller 190 mayreceive measurements from the battery monitor 172, a temperature sensor194 and a pressure sensor 196. The system 100 further includes a centralcontrol unit (CCU) 160 that may serve as a hub for determiningnavigational parameters based on sonar measurements and othernavigational and sensor parameters, and for controlling the movement ofthe vehicle.

In the context of a surface or underwater vehicle, the CCU 160 maydetermine navigational parameters such as position (latitude andlongitude), velocity (in any direction), bearing, heading, accelerationand altitude. The CCU 160 may use these navigational parameters forcontrolling motion along the alongtrack direction (fore and aft),acrosstrack direction (port and starboard), and vertical direction (upand down). The CCU 160 may use these navigational parameters forcontrolling motion to yaw, pitch, roll or otherwise rotate the vehicle.During underwater operation, a vehicle such as an AUV may receivehigh-frequency real aperture sonar images or signals at sonar unit 110,which may then be processed, filtered, corrected, and correlated againsta synthetic aperture sonar (SAS) map of the terrain. Using thecorrelation, the CCU may then determine the AUV's position, withhigh-precision and other navigational parameters to assist withnavigating the terrain. The precision may be determined by the signaland spatial bandwidth of the SAS map and/or the acquired sonar image. Incertain implementations, assuming there is at least a near perfectoverlap of the sonar image with a prior SAS map with square pixels, andassuming that the reacquisition was performed with a single channelhaving a similar element size and bandwidth, and assuming little or nolosses to grazing angle compensation, the envelope would be aboutone-half the element size. Consequently, in certain implementations, thepeak of the envelope may be identified with high-precision, includingdown to the order of about 1/100^(th) of the wavelength. For example,the resolution may be less than 2.5 cm, or less than 1 cm or less thanand about 0.1 mm in the range direction.

Generally, terrain recognition using long wavelength (low-frequency)sensors may be difficult due to the aspect dependence of objectsignatures. Sonar or radar images may be dominated by speckle thatchange with both sonar and object aspect, making incoherent imagecorrelation extremely difficult. Coherently, any correlation operationinvolving signals with non-overlapping frequency bands will yield ananswer of zero (since correlation is multiplication in the frequencydomain). For two sonar images to correlate it is not enough that theirspatial frequencies overlap, but the same points in the two images mustbe represented at overlapping frequencies. For a generic real aperturesonar, the same signature for a complex scene can only typically bere-observed by revisiting the original observation position andorientation and using the same frequencies. Consequently, in general,getting two complex sonar or radar images to coherently correlate is ameasure zero occurrence; the expected cross correlation can be proven tobe approaching zero. Therefore, coherently navigating relative toterrain is, in general, impossible if the system compares real apertureimagery to prior real aperture imagery, except as described below withreference to FIG. 12. Incoherent navigation is possible (i.e. using onlythe envelope) if there is distinct terrain, but against a uniform bottom(mud flat, field of gravel, ocean floor, etc.) this is usually not so.

Holographic navigation of a terrain, e.g., using a system implemented onAUVs, solves this problem by replacing at least one of the real apertureimages with a synthetic aperture image. Because a synthetic apertureimage is a type of hologram (or quasi-hologram) it contains all possiblereal aperture images over some range of frequencies and angles.Consequently, it may be possible to correlate a real aperture imageagainst the synthetic aperture image and have a non-zero expected crosscorrelation. However, according to the Closed/Open Aperture theorem, itmay be required that the synthetic aperture be a planar syntheticaperture, meaning that it is fully populated and Nyquist sampled in twodimensions. This type of population and sampling frequency is, ingeneral, impractical.

By assuming the terrain is a manifold with embedded scatterers on thesurface, and avoiding sub-bottom profiles/operating above the criticalangle, or operating below the critical angle where the SNR is low, it ispossible to show that the planar aperture can be replaced with a contouraperture provided the frequencies can rescaled. For example, consider anactive sonar or radar and two scatterers spaced 5 centimeters apart inrange on a flat bottom. From the perspective of a sonar or radar lookingat the scatterers from the ground, the distance of travel for the twoechoes differ by 10 cm (out and back). If the observer is, instead,looking down at an angle of 45 degrees above horizontal, the differenceis shorted by cosine of 45 degrees (half) to 7.07 cm. So at horizontal a10 cm wavelength would be exactly one cycle out of phase (constructivelyinterferes), and a 20 centimeter wavelength would be exactly a halfcycle out of phase (destructively interfere). At 45 degrees, the samewould be true of a 7.07 cm wavelength and a 14.14 cm wavelength. Bothwavelengths are scaled by the same amount (and, similarly, so arefrequencies, except inversely). More generally, a change in verticalangle shifts all frequencies and changes the signal length by the cosineof the angle. This is not a shift in frequency so much as a change inpitch, where a doubling in frequency corresponds to a change in pitch ofone octave. So by changing the observation angle from horizontal tolooking down at 60 degrees the expected return is shorted by half andincreases in pitch by one octave. In order for this to work, it isnecessary for the second observation to be made with appropriatelyscaled frequencies relative to the first; for a very narrowband systemtoo much of a change in grazing angle simply leads to the knownsignatures being out of band.

In some configurations, using grazing angle compensation and a priorsynthetic aperture image of the systems and methods described herein, itis possible to navigate relative to terrain using a single element sonaror radar. Although synthetic aperture systems are extremely expensive,single element systems are generally very cheap. This means a veryexpensive mapping system can enable the widespread use of cheapautonomous systems with minimal inertial navigation. However, successfulholographic navigation implementations to date have all used lowfrequency sonars (i.e. under 50 kHz), while the higher frequency systemshave not worked. This is unfortunate, because lower frequencytransmitters are, in general, larger, higher power, and more expensive.Thus, it is desirable to have a high frequency single elementholographic navigation system. Further illustrative implementations ofholographic navigation systems and methods are disclosed in U.S. patentapplication Ser. Nos. 12/802,453, 12/454,486, 12/454,484, 12/454,885,13/466,059, 13/466,062, 13/466,063, 13/466,067, 13/466,067, 13/466,075,and 13/466,078, the contents of each of which are incorporated herein byreference in their entirety.

In one aspect, the invention relates to a method of terrain relativelocalization via holographic navigation. Holographic navigation andholographic maps are further described in U.S. patent application Ser.Nos. 12/798,169 and 12/802,455, the contents of each of which areincorporated herein by reference in their entirety. In some respects,holographic navigation is a method of terrain relative localization thattakes advantage of the holographic properties of sonar and radar images.Quite often such terrain relative localization is performed by a systemimplemented on an autonomous underwater vehicle (AUV). However, theperformance of holographic navigation algorithms implemented on suchsystems may degrade substantially as frequencies increase andwavelengths decrease. Conventionally, it is generally assumed that suchdegradation is because the some of the assumptions of grazing anglecompensation break down. In other words, it is assumed that a change invertical aspect no longer maps to a pure change in pitch becauseshadowing, occlusion, and complex three dimension relief fundamentallychange the signature. However, the inventor has recently recognized thatthis assumption is not entirely incorrect, and that holographicnavigation may fail at higher frequencies due to spatially varying phaseerrors. In some implementations, the invention corrects for those rangevarying phase errors by allowing for holographic navigation at higherfrequencies with lower power consumption and smaller sized hardware.

As noted above, the system 100 includes a sonar unit 110 fortransmitting and receiving acoustic signals. The sonar unit includes atransducer array 112 having a one or more transmitting elements orprojectors and a plurality of receiving elements arranged in a row. Incertain implementation, the transducer array 112 includes separateprojectors and receivers. The transducer array 112 may be configured tooperate in SAS mode (either stripmap or spotlight mode) or in a realaperture mode. In certain implementations, the transducer array 112 isconfigured to operate as a multibeam echo sounder, sidescan sonar orsectorscan sonar. One example of a transducer array is shown in FIG. 3having one transmitting elements and six receiving elements. Thetransmitting elements and receiving elements may be sized and shaped asdesired and may be arranged in any configuration, and with any spacingas desired without departing from the scope of the present disclosure.As described later in the present disclosure the number, size,arrangement and operation of the transducer array 112 may be selectedand controlled to insonify terrain and generate high-resolution imagesof a terrain or object. One example of an array 112 includes a 16channel array with 5 cm elements mounted in a 12¾ inch vehicle.

The sonar unit 110 further includes a receiver 114 for receiving andprocessing electrical signals received from the transducer, and atransmitter 116 for sending electrical signals to the transducer. Thesonar unit 110 further includes a transmitter controller 118 forcontrolling the operation of the transmitter including the start andstop, and the frequency of a ping.

The signals received by the receiver 114 are sent to a preprocessor forconditioning and compensation. Specifically, the preprocessor 120includes a filter conditioner 122 for eliminating outlier values and forestimating and compensating for hydrophone variations. The preprocessorfurther includes a Doppler compensator 124 for estimating andcompensating for the motion of the vehicle. The preprocessed signals aresent to a matched filter 130.

The matched filter 130 includes a pulse compressor 132 for performingmatched filtering in range, and a beamformer 134 for performing matchedfiltering in azimuth and thereby perform direction estimation.

The signal corrector 140 includes a grazing angle compensator 142 foradjusting sonar images to compensate for differences in grazing angle.Typically, if a sonar images a collection of point scatterers the imagevaries with observation angle. For example, a SAS system operating at afixed altitude and heading observing a sea floor path will producedifferent images at different ranges. Similarly, SAS images made at afixed horizontal range would change if altitude were varied. In suchcases, changes in the image would be due to changes in the grazingangle. The grazing angle compensator 142 is configured to generategrazing angle invariant images. One such grazing angle compensator isdescribed in U.S. patent application Ser. No. 12/802,454 titled“Apparatus and Method for Grazing Angle Independent Signal Detection,”the contents of which are incorporated herein by reference in theirentirety.

The signal corrector 140 includes a phase error corrector 144 forcorrecting range varying phase errors. The phase error corrector 144 maycorrect for phase error using a technique described with reference toFIG. 7. Generally, the phase error corrector 144 breaks the image upinto smaller pieces, each piece having a substantially constant phaseerror. Then, the phase error may be estimated and corrected for each ofthe smaller pieces.

The system 100 further includes a signal detector 150 having a signalcorrelator 152 and a storage 154. The signal detector 150 may beconfigured to detect potential targets, estimate the position andvelocity of a detected object and perform target or pattern recognition.In one implementation, the storage 154 may include a map store, whichmay contain one or more previously obtained SAS images real apertureimages or any other suitable sonar image. The signal correlator 152 maybe configured to compare the received and processed image obtained fromthe signal corrector 140 with one or more prior images from the mapstore 154.

The system 100 may include other components, not illustrated, withoutdeparting from the scope of the present disclosure. For example, thesystem 100 may include a data logging and storage engine. In certainimplementations the data logging and storage engine may be used to storescientific data which may then be used in post-processing for assistingwith navigation. The system 100 may include a security engine forcontrolling access to and for authorizing the use of one or morefeatures of system 100. The security engine may be configured withsuitable encryption protocols and/or security keys and/or dongles forcontrolling access. For example, the security engine may be used toprotect one or more maps stored in the map store 154. Access to one ormore maps in the map store 154 may be limited to certain individuals orentities having appropriate licenses, authorizations or clearances.Security engine may selectively allow these individuals or entitiesaccess to one or more maps once it has confirmed that these individualsor entities are authorized. The security engine may be configured tocontrol access to other components of system 100 including, but notlimited to, navigation controller 170, motor controller 180, sensorcontroller 190, transmitter controller 118, and CCU 160.

Generally, with the exception of the transducer 112, the variouscomponents of system 100 may be implemented in a computer system, suchas computer system 200 of FIG. 2. More particularly, FIG. 2 is afunctional block diagram of a general purpose computer accessing anetwork. The holographic navigation systems and methods described inthis application may be implemented using the system 200 of FIG. 2.

FIG. 2 is block diagram of an exemplary computer system for implementingat least a portion of the systems and methods described in the presentdisclosure. The exemplary system 200 includes a processor 202, a memory208, and an interconnect bus 218. The processor 202 may include a singlemicroprocessor or a plurality of microprocessors for configuringcomputer system 200 as a multi-processor system. The memory 208illustratively includes a main memory and a read-only memory. The system200 also includes the mass storage device 210 having, for example,various disk drives, tape drives, etc. The main memory 208 also includesdynamic random access memory (DRAM) and high-speed cache memory. Inoperation and use, the main memory 208 stores at least portions ofinstructions for execution by the processor 202 when processing data(e.g., model of the terrain) stored in main memory 208.

In some implementations, the system 200 may also include one or moreinput/output interfaces for communications, shown by way of example, asinterface 212 for data communications via the network 216. The datainterface 212 may be a modem, an Ethernet card or any other suitabledata communications device. The data interface 212 may provide arelatively high-speed link to a network 216, such as an intranet,internet, or the Internet, either directly or through another externalinterface. The communication link to the network 216 may be, forexample, any suitable link such as an optical, wired, or wireless (e.g.,via satellite or 802.11 Wi-Fi or cellular network) link. In someimplementations, communications may occur over an acoustic modem. Forinstance, for AUVs, communications may occur over such a modem.Alternatively, the system 200 may include a mainframe or other type ofhost computer system capable of web-based communications via the network216.

In some implementations, the system 200 also includes suitableinput/output ports or may use the Interconnect Bus 218 forinterconnection with a local display 204 and user interface 206 (e.g.,keyboard, mouse, touchscreen) or the like serving as a local userinterface for programming and/or data entry, retrieval, or manipulationpurposes. Alternatively, server operations personnel may interact withthe system 200 for controlling and/or programming the system from remoteterminal devices (not shown in the Figure) via the network 216.

In certain implementations, a system implementing high frequencyholographic navigation requires a processor, such as a navigationalcontroller 170, coupled to one or more coherent sensors (e.g., a sonar,radar, optical antenna, etc.) 214. Data corresponding to a model of theterrain and/or data corresponding to a holographic map associated withthe model may be stored in the memory 208 or mass storage 210, and maybe retrieved by the processor 202. Processor 202 may executeinstructions stored in these memory devices to perform any of themethods described in this application, e.g., grazing angle compensation,or high frequency holographic navigation.

The system may include a display 204 for displaying information, amemory 208 (e.g., ROM, RAM, flash, etc.) for storing at least a portionof the aforementioned data, and a mass storage device 210 (e.g.,solid-state drive) for storing at least a portion of the aforementioneddata. Any set of the aforementioned components may be coupled to anetwork 216 via an input/output (I/O) interface 212. Each of theaforementioned components may communicate via interconnect bus 218.

In some implementations, a system implementing high frequencyholographic navigation requires a processor coupled to one or morecoherent sensors (e.g., a sonar, radar, optical antenna, etc.) 214.Examples of suitable sensor arrays are illustrated schematically inFIGS. 3 and 4. An exemplary sonar array is shown in FIG. 3. This arrayincludes a transmitter, receive array, and receive element. An exemplarysynthetic aperture sonar array is shown in FIG. 4. This array includes atransmitter, receive array, and receive element, and a virtual arraywith an associated phase center/virtual element.

Data corresponding to a model of the terrain, data corresponding to aholographic map associated with the model, and a process for grazingangle compensation may be performed by a processor 202 operating on thedata, as shown in FIG. 2. The system may include a display 204 fordisplaying information, a memory 208 (e.g., ROM, RAM, flash, etc.) forstoring at least a portion of the aforementioned data, and a massstorage device 210 (e.g., solid-state drive) for storing at least aportion of the aforementioned data. Any set of the aforementionedcomponents may be coupled to a network 216 via an input/output (I/O)interface 212. Each of the aforementioned components may communicate viainterconnect bus 218.

In operation, a processor 202 receives a position estimate for thesensor(s) 214, a waveform or image from the sensor(s) 214, and datacorresponding to a model of the terrain, e.g., the sea floor. In someimplementations, such a position estimate may not be received and theprocess performed by processor 202 continues without this information.Optionally, the processor 202 may receive navigational informationand/or altitude information, and a processor 202 may perform a coherentimage rotation algorithm. The output from the system processor 202includes the position to which the vehicle needs to move to.

The components contained in the system 200 are those typically found ingeneral purpose computer systems used as servers, workstations, personalcomputers, network terminals, portable devices, and the like. In fact,these components are intended to represent a broad category of suchcomputer components that are well known in the art.

It will be apparent to those of ordinary skill in the art that methodsinvolved in the systems and methods of the invention may be embodied ina computer program product that includes a non-transitory computerusable and/or readable medium. For example, such a computer usablemedium may consist of a read only memory device, such as a CD ROM disk,conventional ROM devices, or a random access memory, a hard drive deviceor a computer diskette, a flash memory, a DVD, or any like digitalmemory medium, having a computer readable program code stored thereon.

Optionally, the system may include an inertial navigation system, aDoppler sensor, an altimeter, a gimbling system to fixate the sensor ona populated portion of a holographic map, a global positioning system(GPS), a long baseline (LBL) navigation system, an ultrashort baseline(USBL) navigation, or any other suitable navigation system.

High-Frequency Holographic Navigation

FIGS. 5A-5B depict processes 500 and 550 for navigating a terrain usingan exemplary high-frequency sonar navigation system, such as system 100.In particular, the processes 500 and 550 may be implemented acrossseveral components of system 100 of FIG. 1. The system 100 may receivevia wire or wirelessly, at the map store 154, a prior high frequency SASimage of a portion of the terrain being navigated (step 502). The priorimage may have been obtained using a frequency range greater than 100kHz. For example, the frequency of the prior SAS image may include awell-formed image in the frequency range of 100 kHz-110 kHz, or between110 kHz-120 kHz. The frequency of the prior SAS image may be between 100kHz-150 kHz and/or 125 kHz-175 kHz and/or 175 kHz-225 kHz. The frequencyof the prior SAS image may be greater than 500 kHz and in certainimplementations, the frequency may be greater than 1 MHz. In certainimplementations, the frequency ranges may be selected based onapplication. For example, for certain ocean systems, the frequencies maybe up to about 500 kHz, and in certain medical ultrasound systems, thefrequency may be about 15 MHz. In certain implementations, the frequencyranges may be selected to be less than 100 kHz. In such implementations,the process 500 may be especially beneficial depending on the ratio ofthe size of the error to the wavelength. In one example, for shipsbouncing in waves process 500 may be beneficial for frequencies downbelow 10 kHz. The prior SAS image may be grazing angle compensatedand/or phase error corrected and the frequency of the image may bepost-grazing angle correction. In certain alternative implementations,the prior image may include a low frequency image in the tens of kHz andless than 100 kHz.

The process 500 includes predicting, by the CCU 160, an initial positionvalue of the vehicle traversing the terrain based on a previous position(Step 504). The position value may be represented in any suitablecoordinate system. The CCU 160 may generate this initial position valuebased on information from the navigational controller 170 and previousmotion. The CCU 160 may also determine an error estimate or navigationaluncertainty associated with this initial position value (step 506).

The system 100 may insonify a portion of the terrain being navigatedwith a high-frequency signal and generate a current sonar image (step508 in FIG. 5A, step 558 in FIG. 5B). In certain implementations, CCU160 in connection with the transmitter controller 118 may sendtransmission instructions to the transmitter 116 and the transducerarray 112. To allow for coherent correlation, the frequency of imagingmay be selected to overlap with the frequency range of the receivedprior map obtained in step 502. The overlap in frequencies may be acomplete overlap, a partial overall or an implicit overlap. In acomplete overlap, the frequency range of the current sonar image may liecompletely within the frequency range of the prior map. In a partialoverlap, the frequency range of the current sonar image may partiallyoverlap with the frequency range of the prior map. Even when frequencyranges of the current raw imaging process and the prior mapping processdo not overlap, there may still be an implicit overlap if the aspect orviewing angle of the two images are appropriately different. In such animplicit overlap scenario, the grazing angle compensated frequencies ofthe current image and the prior map at least partially overlap. Forexample, a 100 kHz signal at a 45 degree grazing angle would have thesame projected wavelength as a 70.7 kHz signal at a grazing angle ofzero and would consequently constitute an implicit overlap. As anotherexample, a 100 kHz-110 kHz image at a relatively flat grazing angle maycoherently correlate with a 110 kHz-120 kHz map at a relatively steepergrazing angle. Generally, the system 100 may operate as a SAS and obtaina high-frequency SAS image. To obtain a SAS image, the system 100 mayoperate in sectorscan mode, sidescan mode, stripmap mode and/orspotlight mode. System 100 may even operate as a phased array and obtaina real aperture image of the terrain.

The obtained current image, which may include a real aperture image, instep 508 in FIG. 5A or a synthetic aperture image, in step 558 in FIG.5B, may be passed through preprocessor 120, matched filter 130 andreceived at the signal corrector 140. The obtained current image ismodified to compensate for grazing angle by the grazing anglecompensator 142 (step 509 in FIG. 5A, step 559 in FIG. 5B). Generally,the obtained current image is converted to a grazing angle invariantimage. The grazing angle compensator 142 approximates the terrain (e.g.,sea floor) as a smoothly undulating manifold with embedded pointscatterers, and models the sonar signals as interference between pointscatterer echoes. Shadowing and occlusion are generally neglected andchanges in grazing angle are assumed to change the pitch of the echo.Changes in pitch generally cause all frequencies to be scaled by amultiplier which is the secant of the grazing angle. By reversing theprocess (i.e., projecting the echo onto the sea floor), a relationshipbetween scatterer spacing and image frequency is established that isindependent of grazing angle. Typically, grazing angle compensation islimited by transmitter design; the applicable range of angles isdetermined by signal bandwidth and transmitter properties.

The compensated obtained current image, which may be a real apertureimage or a synthetic aperture image or any suitable sonar image, is thenmodified to correct for range varying phase errors by the phase errorcorrector 144 (step 510 in FIG. 5A, step 560 in FIG. 5B). The process ofcorrecting for range varying phase errors, which allows forhigh-frequency imaging and navigation, is described in more detail withreference to FIG. 7.

The compensated and error corrected obtained current image of theterrain is coherently correlated, at the signal correlator 152, with theprior SAS map received at step 502 (step 511 in FIG. 5A, step 561 inFIG. 5B). Generally, because image intensities can spatially vary, thesignal detector 150 may be configured to perform a normalizedcorrelation. In certain implementations, the normalized correlation maybe performed by calculating the correlation coefficient. Generally forsonar images, the correlation coefficient is often low (less than 0.1)and the values depend on the available structure. Without a prioriknowledge of the terrain, it is difficult to define detector thresholds.Detection may still be possible, however, because signal to noise ratios(SNR) may be high. The signal detector 150 may calculate additionalstatistics of the normalized correlation include the statisticaldistributions of the signals (amplitude and phase) and/or noise. Thestatistical distributions may include Rayleigh and/or Gaussiandistributions. The detector thresholds may be selected based on thedistribution. Examples of suitable correlation techniques included insignal detector 150 techniques described in “On Correlating SonarImages,” Richard J. Rikoski and J. Tory Cobb and Daniel C. Brown,Robotics: Science and Systems'05, 2005, and “Holographic navigation,”Richard J. Rikoski and Daniel C. Brown, ICRA'08, 2008, the entirecontents of each of which are incorporated herein by reference.

Using the coherent correlation of the image with the map, the CCU 160may determine a measured position value (step 512) and the associatederror estimate of the position (step 513). Certain exemplary techniquesto determine measured position are described in “Holographicnavigation,” Richard J. Rikoski and Daniel C. Brown, ICRA'08, 2008, theentire contents of which are incorporated herein by reference.

Based on the new position estimate, the CCU 160 may update controlsignals and instruct the motor controller 180 to move the vehicleaccordingly. If navigation to a new location is complete (step 515), theprevious map-based calculated position is set as the previous position(step 516) and the process 500 is repeated at the new position.

In some implementations, traditional SAS navigation may robustly andeasily solve for position, but may less efficiently solves for heading,e.g., of an unmanned autonomous vehicle. It may be possible to correlatea synthetic aperture image against either a real aperture or syntheticaperture image at various angles to estimate the heading, but this maybe computationally intensive. The proposed system may solve this problemby decomposing the heading estimation problem into a two step process.First, holographic navigation is used to estimate position. Then, acorrelation is performed using an angular coordinate system centered onthe estimated position. Assuming range is r and angle is θ, a coordinatesystem which is a function of r and θ is used for correlation. In thesimplest instantiation, (fr), g(θ))=(r, θ), but alternatives likef(r)=horizontal distance along the bottom or g(θ)=sin(θ) may also beappropriate. The correlation may either be in range and angle or just inangle, but to detect heading it may be necessary to correlate in angle.The angle with the maximum correlation corresponds to the direction thereacquisition sonar is facing. Two exemplary processes for solving forheading are processes 600 and 650 illustrated in FIG. 6A and FIG. 6b ,respectively. Processes 600 and 650 may be similar to processes 500 and550, respectively, except for the step of determining heading based onmeasured position value as shown in step 602 in FIG. 6A and step 652 inFIG. 6B.

As noted above, the system 100 includes a phase error corrector 144 tocorrect for range varying phase errors. Range varying phase errors maylead to low correlation values when correlating between two highfrequency images (e.g., an image and a map). As an illustrative example,suppose a robot with a high frequency sonar attempts to correlate itsimagery with a prior map but it has a 1 cm altitude error and a 1 cmwavelength. Directly underneath the vehicle this leads to a 2 cm pathlength error, or 2 cycles. At long range, this leads to a zero cycledelay. Consequently, if the prior sonar image and the conjugate of thenew sonar are multiplied together but not summed (image1*conj(image2))what will be observed is a range varying phase that is due to thataltitude error. When the multiplied images are summed together thisrange varying phase error will cause destructive interference, leadingto a very low correlation value.

Similarly, as a second illustrative example, assume a 0.01% sound speederror, a 1 cm sonar, and an operating altitude of 5 meters. The travelpath directly under the vehicle is 10 meters, or 1000 cycles, leading toa 1/10^(th) of a cycle error. At a range of 50 meters (or 10,000 cycles)this leads to a full cycle error. At a range of 500 meters this leads to10 cycles of error. So again, when the multiplied sonar images aresummed constructive interference will drive the cross correlation down.High frequency holographic navigation attempts to solve these problemsby either using image pieces which are small enough to be immune tothose effects, or by estimating and correcting for those biasedparameters.

One method of correcting for phase error is to cut the image into smallregions where the phase error is constant and use those as independentmeasurements. In regions with very high signal-to-noise ratio (SNR) thismay be very efficient.

FIG. 7 depicts a process 700 for correcting range varying phase errorsin a high-frequency sonar system. The process 700 may be implemented onphase error corrector 144 of the signal corrector 140 in system 100 ofFIG. 1. The process 700 begins with receiving a real aperture image orsynthetic aperture image. (step 702). In certain implementations, thereal aperture image may be modified with grazing angle compensation. Thephase error corrector 144 may estimate the range varying phase error ofthe entire received real aperture image (step 704). The phase errorcorrector 144 may then determine if the variation in phase error acrossthe image is less than an error threshold (step 706). The errorthreshold may be set as desired. In certain implementations, the errorthreshold may be set depending on the maximum range of the real apertureimage. If the variation in phase error across the image is greater thanthe error threshold then the image may be split into sub-regions (step708). The phase error corrector 144 may split the image into sub regionsas desired. The sub-regions may be of equal sizes or of different sizes.Sub-regions may be of varying sizes such that the size variation may bebased on the range. The phase error corrector 144 may estimate the phaseerror for each sub-region (step 712), and determine if the variation inphase error across each subregion is less than a phase error threshold(step 714). The error threshold for subregions may be the same as ordifferent from (greater than or less than) the error thresholdassociated with step 706. The error thresholds for each subregion may bedifferent or the same. If the phase error in a particular subregion isless than the error threshold, that particular subregion may becorrected for the corresponding phase error, which is substantiallyconstant across the entirety of the particular subregion (step 715). Ifthe phase error is greater than the threshold then the subregion may besplit into smaller subregions and steps 708, 712, 714 may be repeated.In certain implementations, the subregions may be selected such thatthey have constant altitude phase error or constant sound speed error.One or more selected subregions in one or more sensitive mapping regionsmay be selected to have the largest possible size.

In some implementations, suppose system 100 can correlate small patches(e.g., 50 pixels by 50 pixels). In such example, if the image is 1000 by1000 pixels, then system 100 may cut the image up into 20×20 regions of50×50 pixels each. The system 100 may perform 400 separate correlations.Each correlation may have a peak with a slight shift and a slightlydifferent phase value due to the unknown error function. System 100 maytake the absolute value of each correlation and sum them all together toeliminate destructive interference due to the phase differences. Such anapproach may be advantageous at least when the error function isunknown. Also, although the noise may be Rayleigh distributed when thedistribution of the absolute value for a single image correlation isviewed, but when system 100 sums a large number together the law oflarge numbers applies and the noise becomes Gaussian distributed.

The system 100 may include other methods for compensating for phaseerrors. In certain implementations, the real image is taken and a sum ofthe envelopes of small image correlation regions with approximatelystationary phase is calculated before calculating a probability densityfunction based on that sum. This may be similar to a speckle reductiontechnique used in imaging methods. The sums can either be for a singlealtitude solution or for multiple altitude solutions; if multiplealtitude solutions are used then the technique measures altitude bias.In certain implementations, using the envelope only (the absolute valueof the correlation result) removes the relative phase differencesbetween correlation results. It is important to note that summingtogether a large number of correlation images results in a transitionfrom Rayleigh to Gaussian distributed speckle intensity; this differencemay be important when converting the correlation result to a probabilitydensity function. Previous holographic navigation techniques, which usethe Rayleigh distribution, typically fail when presented withcorrelation results based on sub-image summation; switching to a morerepresentative distribution is key. When a small number of images aresummed together the distribution may not yet be fully Gaussian and maybe better represented by some other distribution such as aK-distribution.

In some implementations, patches may be used with roughly stationaryphase to estimate the range varying phase error and then apply anappropriate correction so as to enable full waveform correlation. Insome implementations, estimating the range varying phase error may bedone several ways, including, inter alia, unwrapping the phase andfitting a curve, doing a least squares fit to the raw angles, orchanging coordinate systems and using a Fast Fourier Transform (FFT) orany type of fourier transform such as a Discrete Fourier Transform (DFT)or a wavelet transform to find the delay. This implementation (combininga coordinate system change and an FFT) is applicable to time delayestimation beyond holographic navigation (for instance, to motionestimation for synthetic aperture systems using displaced phased centernavigation, especially for heave estimation).

In one example of a heave estimation technique includes the following:

$t = {\frac{2r}{c} = \frac{2\sqrt{x^{2} + z^{2}}}{c}}$Where t=time, r=range, c=sound speed, x=horizontal range and z=altitude.

$x = \sqrt{\frac{c^{2}t^{2}}{4} - z^{2}}$$\frac{dx}{dz} = {{- \frac{z}{\sqrt{\frac{c^{2}t^{2}}{4} - z^{2}}}} = {{{{- \frac{z}{x}}x^{\prime}} \cong {x + e_{x} + {\frac{dx}{dz}e_{z}}}} = {x + e_{x} - {\frac{z}{x}e_{z}}}}}$Where e_(x)=x error, e_(z)=z error, s˜=envelope function,k_(x0)=wavenumber of carrier frequency.

Convert s(t)->s(x)

Now, s1(x1) !=s2(x2) because of error (e_(x), e_(z)). So when youmultiply s1(x1) by the conjugate of s2(x2), the signals differ by anoffset:

${x\; 2} = {{x\; 1} + e_{x} - {\frac{z}{x\; 1}e_{z}}}$${{x\; 2} - {x\; 1}} = {e_{x} - {\frac{z}{x\; 1}e_{z}}}$

Now, e_(x) is usually pretty easily observable and removable, gettingyou to a range varying error:

${{x\; 2} - {x\; 1}} = {\frac{- z}{x\; 1}e_{z}}$

The signals are now

${s\; 1(x)} = {\overset{\sim}{s\; 1}(x)e^{{jk}_{x\; 0}x}}$${s\; 2(x)} = {{\overset{\sim}{s\; 2}(x)e^{{jk}_{x\; 0}x}} = {\overset{\sim}{s\; 1}( {x + {\frac{dx}{dz}e_{z}}} )e^{{jk}_{x\; 0}{({x + {\frac{dx}{dz}e_{z}}})}}}}$${s\; 1(x)s\; 2^{*}(x)} = {\overset{\sim}{s\; 1}(x)\overset{\sim}{s\; 2}(x)e^{{- {jk}_{x\; 0}}\frac{dx}{dz}e_{z}}}$${s\; 1(x)s\; 2^{*}(x)} = {\overset{\sim}{s\; 1}(x)\overset{\sim}{s\; 2}(x)e^{{jk}_{x\; 0}\frac{z}{x}e_{z}}}$

Now, changing into a new coordinate system

$\sigma = \frac{k_{x\; 0}z}{x}$changes this intos1(σ)s2*=

(σ)

(σ)e ^(jσe) ^(z) ≅

(σ)² e ^(jσe) ^(z)From here, we can do a Fourier transform to estimate e_(z).

In certain implementations, one or more techniques employed by thesignal corrector 140 or any other component of system 100 may be used incertain applications including, but not limited to, change detection andtwo pass interferometry. Change detection is typically a process oftaking two passes by a scene, then accurately aligning two images, andcoherently comparing them. In regions where there has been “change” theymay decorrelate significantly. Two-pass interferometry is typically aprocess of taking two passes over a scene, aligning two images andcomparing the phase of these two images. The comparison of phase of twoimages may reveal changes and deformation in the terrain. In certainimplementations, such deformation may be over timespans of days toyears. Such applications may be useful for geophysical monitoring ofnatural hazards, such as earthquakes, volcanoes and landslides, and alsoin structural engineering, including monitoring of subsidence andstructural stability. Other applications of system 100, and particularlysignal corrector 140, include reconnaissance, surveillance andtargeting. These applications may use system 100 to generate highresolution images and to distinguish terrain features (surface and/orunderwater) and to recognize and identify selected man made targets.Still other applications include interferometry, navigation, guidance,imaging foliage and underground or subsurface targets, detecting andmoving targets, and environmental monitoring application such asmonitoring oil spills.

They systems and methods described herein may be adapted as desired forboth sonar and radar systems. For example, sonar transducers may bereplaced with suitable radar transducers, and one or more components maybe modified, added to or removed from the systems described herein tooperate in a sonar and radar regime. In some implementations, thesystems and methods may be configured to operate as both sonar and radardevices, without departing from the scope of the present disclosure. Incertain implementations, when the systems and methods are configured forsonar imaging, the frequencies may be in the range from 100 kHz to about200 kHz. In certain implementations, when the systems and methods areconfigured for radar imaging, the frequencies may be in the range from 1GHz to about 30 GHz. Generally, the systems and methods described hereinmay be applied for any frequency range, without departing from the scopeof the present disclosure.

Certain Applications of High-Frequency Holographic Mapping andNavigation Systems

The systems and methods described here make use of various other aspectsof the holographic nature and high frequency of synthetic apertureimages, which inventor has recognized. For example, systems and methodsare described herein for determining a three-dimensional model of ashape based on its two dimensional shading and shadowing of acousticsignals. In some implementations, traditional shape from shading may beone parameter shy of a solution; an approximation may be required inorder to derive a three dimensional model from a pure image. However,since a synthetic aperture image is a quasi-hologram and contains acontinuum of images of a range of angles, it may contain enoughinformation to over-constrain the shape from shading problem. Thesystems and methods above described solve the shape from shading problemby decomposing the SAS image into lower resolution sub-patches and thenderiving their orientation from the shading observed from multiplevantage points.

The systems and methods described herein include methods for positioningsensors (such as Tsunami sensors) and navigation beacons withhigh-precision using HF holographic navigation. In certainimplementations, Tsunami buoys use sensors on the seafloor to smallvariations in water pressure. To make accurate measurements it isnecessary that the sensor be positioned properly on the seafloor. If thesensors are hidden behind rocks or are not level/well placed on the seafloor it can affect the accuracy of their measurements. The systems andmethods described herein combine a holographic navigation system, andmaneuvering system, and a tsunami sensor so that tsunami sensors can bepositioned very precisely using a prior map.

In some implementations, holographic navigation enables very highprecision navigation relative to the seafloor, but may be limited by thenecessity of periodically observing the seafloor. Midwater system thatmay be at a great altitude to observe the seafloor cannot take advantageof holographic navigation or its precision. The systems and methodsdescribed herein address this limitation by combining a beacon system,and maneuvering system, and a holographic navigation system. The beaconis able to position itself very precisely, enabling systems includinglong baseline navigation or ultrashort baseline navigation withoutneeding to calibrate the beacon system using a ship.

The systems and methods described herein include methods for monitoringand modeling a water column using an autonomous underwater vehicle (AUV)based on high-precision location measurements obtained using HFholographic navigation. In some implementations, AUVs either circle abuoy or simply form a wagon wheel. By transmitting orthogonal signals toone another they can measure time of flight between positions and alsomeasure differential time of flight. From time of flight, it may bepossible to determine the sound speed of the water, from differentialtime of flight it may be possible to determine the Doppler shift alongthe connecting vector/estimate water velocity. Vehicle positions aredetermined using holographic navigation, thereby enabling a highprecision model of the water column in post processing. Vehicles maydock at a central buoy for recharging.

In some implementations, AUV vehicle recovery and vehicle docking may bedifficult problems due to the dynamic nature of both the vehicle and thedestination. If it can be decomposed into a purely relative problem, thevehicle needs its position relative to the dock as well as itsorientation. The systems and methods described herein allow a vehicle topassively estimate its non-range position relative to a docking systempassively, and allows it to estimate its range to the docking systemactively.

In some implementations, the system takes advantage of the fact that ablazed array transmits different frequencies at different angles. Usingtwo blazed arrays with different frequencies oriented orthogonally itcreates a two dimensional grid of frequencies. For instance, suppose a300-600 kHz blazed array was oriented such that the frequencies variedwith horizontal displacement, and a 600-1200 kHz blazed array wasoriented such that its frequencies varied in the vertical direction. Avehicle observing 450 kHz and 900 kHz would be driving straight into thedock. A system observing 500 kHz and 900 kHz would have the correctelevation but would be displaced horizontally. A system observing 450kHz and 950 kHz would be displaced vertically. In some implementations,to measure the vehicle's orientation with respect to the dockingstation, the vehicle would have a small passive array to measure thedirection of the incoming signal from the blazed arrays.

In some configurations, range may be measured using a small beaconsystem such as an ultra short baseline beacon. In some implementations,range may be measured using high frequencies that are only observable atshort ranges, or may be neglected entirely (purely a glide path baseddocking method).

Seismic Survey System Using Planar SAS and Holographic Navigation

Seismic survey is generally a form of 2D or 3D geophysical survey thatis used to measure terrestrial or extra-terrestrial properties by meansof acoustics or electromagnetic. Seismic survey systems are necessaryfor offshore oil exploration, but they are large, ship intensive,expensive, and high power. Traditional seismic survey systems use veryhigh powered transmitters to insonify the bottom, and receive the signalon a network of towed arrays which are dragged behind a large ship.

In certain implementations, the systems and methods include a seismicsurvey system having a combination of orthogonal transmitters andmultiple receivers to form a full planar synthetic aperture sonar withhigher resolution, lower power, and fewer large ships than a traditionalseismic survey system.

Applicants' system takes advantage of the phase center approximation ofsynthetic aperture sonar (SAS). A phase center is located halfwaybetween the transmitter and receiver. For an array to be fully populated(from a Nyquist perspective) it needs to have an appropriate number ofproperly spaced phase centers.

In certain implementations, system 100 includes multiple transmitters.Using multiple transmitters with orthogonal signals, it may be possibleto distinguish between phases created by different transmitters.Therefore, by using M transmitters and N receivers, it is possible tocreate MN phase centers. This is often less expensive than using onetransmitter and MN receivers. In certain implementations, the system 100generalizes to any practical value of M and N.

The transmitters of the system can be mounted on any sort of vessel orrobot (ship, autonomous underwater vehicle (AUV), unmanned surfacevehicle (USV), nuclear submarine, etc). In certain implementations, thetransmitters of the systems described herein may require a relativelyhigh power. In such implementations, the vessel may be equipped withsuitable power delivery systems to supply the needed power to thetransmitters. One example of a vessel includes modest sized USVs such as10 m RHIBs (Rigid Hull Inflatable Boats) since autonomous systems areideal for maneuvering in formation and surface craft enable the use ofGPS.

In certain implementations, system 100 includes multiple receivers. Thereceivers of the system can be mounted on various vehicles and usevarious array types without dragging arrays behind ships (even thoughthis is possible). In some implementations, AUVs are flown in formationclose to the bottom. This reduces losses on the return path, reduces thenecessary transmit power, and allows the receivers to be preciselypositioned using holographic navigation. Holographic navigation in thismanner requires a prior seabed survey of the area, but this isrelatively inexpensive.

In some implementations, the combination of transmitters and receiversform a line array of phase centers. That line array is then translatedorthogonally to its axis in a predominantly horizontal direction, sothat when the data is accumulated there is a Nyquist sampled planararray. Using that planar array it may be possible to beamform the signalto form a 3D image composed of high resolution voxels penetrating deepinto the seabed.

Generally, for seismic survey applications, system 100 may be operatedat any suitable frequency without departing from the scope of thepresent disclosure. For example, system 100 may be configured forfrequencies in the range of 1 Hz to about 10 kHz. The system 100 may begenerally configured for frequencies less than 10 kHz, includingfrequencies in the range of 100 Hz to about 10 kHz. In certainimplementations, system 100 may be adapted with electromagnetictransducers and suitable components for radar-based seismicapplications. In such applications the frequencies may range from about300 MHz to about 30 GHz. Whether configured to operate for radar orsonar based applications, system 100 may use frequencies in any suitablerange without departing from the scope of the invention.

They systems and methods described herein may be adapted as desired forboth sonar and radar systems, and accordingly for both syntheticaperture sonar (SAS) and synthetic aperture radar (SAR) systems. Forexample, sonar transducers may be replaced with suitable radartransducers, and one or more components may be modified, added to orremoved from the systems described herein to operate in a sonar andradar regime. In some implementations, the systems and methods may beconfigured to operate as both sonar and radar devices, without departingfrom the scope of the present disclosure. In certain implementations,when the systems and methods are configured for sonar imaging, thefrequencies may be in both high and low frequency ranges in the rangefrom 10 Hz to about 1 kHz. In certain implementations, when the systemsand methods are configured for radar imaging, the frequencies may be inthe range from 1 MHz to about 100 MHz. Generally, the systems andmethods described herein may be applied for any frequency range, withoutdeparting from the scope of the present disclosure.

Low Grating Sidelobe SAS

The systems and methods described herein include adding multipletransmitters and generating orthogonal pinging sequences configured toenhance the performance of a SAS system. In particular, the systems andmethods described herein include a SAS having a low-grating sidelobe (asdescribed with reference to FIGS. 8-9B), a SAS having a high coveragerate using multiple transmitters (as described with reference to FIG.10-11C), and an overpinging sequence for increasing the range of the SASsystem.

In general, grating sidelobes occur when active sonar elements are oneor more wavelengths apart. Grating sidelobes may not be fully suppressedwhen elements are more than a half wavelength apart. For most activesonar systems this spacing is impractical, since it would require anextremely high channel count and omni-directional elements. Instead,most systems use larger transducer elements with limited beam patterns.The resulting beam pattern of the sonar system is the product of thearray beam pattern (including grating lobes) and the beam pattern of theindividual elements. Since those elements output relatively little inthe direction in the direction of the grating lobes, this spacingpartially suppresses the lobes. According to one illustrativeimplementations, the transmitter and receiver elements have a relevantdimension (e.g, width) d, and assuming a phase center approximation (theellipsoidal travel path between transmitter and receiver is modeled as arange circle centered halfway between the transmitter and receiver), theclassical SAS array has “d/2” spacing, as shown in FIG. 9A.

According to the illustrative implementation of FIG. 9B, one mayarbitrarily increase the array sampling from d/2 to something higher(e.g., d/4), as shown. This spacing takes advantage of the fact that asynthetic aperture sonar system is in constant motion so that it canaccumulate many pings/phase centers so as to create a very highresolution image. The system then operates by transmitting orthogonalsignals after a very short delay to create additional phase centers inbetween the original phase centers so that they can be added inprocessing to create an array with d/2N sampling, where N>1. In effect,the SNR is no longer grating lobe limited.

FIG. 8 depicts a process 800 for using a plurality of orthogonal signalsin a synthetic aperture sonar (SAS) system, such as system 100, togenerate images. In particular, process 800 may be configured to useorthogonal signals to generate SAS beams having suppressed gratinglobes. Process 800 begins with providing a SAS array (such as array 112of FIG. 1) having a transmitting element and plurality of receivingelement (step 802). Such an array is depicted in FIGS. 9A and 9B. Incertain implementations, each of the transmitting and receiving elementsmay have a first dimension, d. The dimension may include any suitabledimension including length, width and diameter. A user or CCU 160 maydetermine the number of orthogonal pings, N (step 804). As noted abovegenerally N>1. In certain implementations, N=2, such that the samplingis about d/4. Each ping p(i)={p1, p2 . . . , pn} has a duration of Tp.In certain implementations, each ping may be orthogonal to one or moreprevious pings such that pings overlapping in time are orthogonal toeach other and non-orthogonal pings do not overlap with each other. TheCCU 160 or the transmitter controller 118 may calculate a firsteffective spacing D=d/2, representative of an effective distance betweentransmitter and receiver elements during motion.

According to process 800, the transmitter controller 118 instructs thetransmitter 116 to set time to t0 and start transmitting the ping p(i),where i=1 (step 808). The transducer array 112 is moved along an axisparallel to that connecting the receiving elements (step 810). Incertain implementations, it may be acceptable for the face of thetransmitting elements or projectors to not be coplanar to the face ofthe receivers. For example, streamlined vehicles include a polyurethanecoating continuous with the body form, however the actual transmittingelements may be embedded about 1-2 inches behind that polyurethanewindow. The CCU 160 determines if the transducer array 112 has moved adistance of D/N(step 812). If the transducer array 112 has moved adistance of DIN, then the CCU 160 determines if all the pings have beentransmitted in the current iteration (step 814). If all the pings havenot been transmitted then, the next ping (which is orthogonal to theprevious ping) is transmitted and the process is repeated from step 810.For example, if N=2, the transmitter width is d=0.1 m, the robot istranslating at 1 meter per second, the time period between the firstping in each cycle is 1 s, and the pulse length is 0.2 s, oneimplementation of the process 800 includes firing the first ping at timet=0. At time, t=0.025 s, the transducer 112 may have translated adistance of d/4. At time, t=0.025 s, the transducer 112 may beconfigured to fire the second orthogonal ping. Between 0.025 s and 0.2s, both the first ping and the orthogonal second ping are transmitting.Between 0 s and 0.025 s, only the first ping is transmitting, andbetween 0.2 s and 0.225 s, only the second orthogonal ping istransmitting.

Generally, and not to be bound by theory, the process allows fordelaying the transmission of the second signal until the vehicle hastranslated enough to create a second virtual array. As was shown inprocess 800, the delay may be related to vehicle speed and firing may beadjusted based on the measured motion while keeping the vehicle speedconstant (“slave to speed” configuration). In certain otherimplementations, the delay may be fixed and the vehicle speed may beadjusted, including performing alongtrack compensation. In anotherconfiguration, the matched filter length may be adjusted slightly tocompensate for alongtrack motion imperfections when defining phasecenters (e.g. in the above example transmitting a noise sequence, butthen dropping the first 0.001 is to 0.00001 s of the matched filtertemplate to correctly place the effective vehicle ping start position.

Consider an exemplary system with a 1 meter long broadside arrayconsisting of 10 centimeter elements and a 10 cm transmitter. Thevirtual array of phase centers is then 50 cm long with phase centersspaced 5 cm apart. In a typical SAS, the vehicle would transmit, move 50cm, and transmit again. For a variant of the present disclosureoperating with d/4 spacing, the vehicle would transmit, move 2.5 cm,transmit an orthogonal signal (so as not to jam the original signal),move an additional 47.5 cm, and then repeat. If the vehicle was movingat 1 m/s, the delay between signals would be 1/40 of a second, or 25 ms.If the transmit signal is longer than 25 ms then the two orthogonalsignals will overlap. In this case, the signals need to be designed suchthat when they are summed together they do not saturate the transmitter.If the goal is higher sampling that d/4, it may be necessary to sumtogether multiple signals.

This method is not only restricted to broadside synthetic aperturesonar. Broadside active phased arrays may use this technique to formvery short aperture synthesis to reduce sidelobes (i.e. a sidescan sonarwould fuse two pings). Real aperture and synthetic aperture forwardlooking and/or squinted sonars may use the same technique to increasetheir element count. The technique would work very well with circularSAS arrays.

Then the signal can be changed from ping to ping to further reducesidelobes after aperture synthesis and to suppress noise from theorthogonal signals. This is manifested in several ways, including.Changing a ping changes its autocorrelation function during aperturesynthesis; summing together different autocorrelation functions withdifferent sidelobe structures will reduce the relative magnitude ofthose sidelobes. Changing the ping changes the cross correlationfunction between the two subcomponent pings so that during aperturesynthesis, the noise is not locally a standing wave and insteaddestructively interferes.

They systems and methods described herein may be adapted as desired forboth sonar and radar systems. For example, sonar transducers may bereplaced with suitable radar transducers, and one or more components maybe modified, added to or removed from the systems described herein tooperate in a sonar and radar regime. In some implementations, thesystems and methods may be configured to operate as both sonar and radardevices, without departing from the scope of the present disclosure. Incertain implementations, when the systems and methods are configured forsonar imaging, the frequencies may be in both high and low frequencyranges in the range from 10 kHz to about 200 kHz. In certainimplementations, when the systems and methods are configured for radarimaging, the frequencies may be in the range from 100 MHz to about 30GHz. Generally, the systems and methods described herein may be appliedfor any frequency range, without departing from the scope of the presentdisclosure.

High Coverage Rate SAS

In certain implementations, the present disclosure relates to a devicefor a synthetic aperture sonar with a real array with N elements of sized which combine to create a real array of length L. Because of the phasecenter approximation, the effective position of the elements is halfwaybetween the transmitter and receivers, making the effective array lengthL/2. This effective array will be referred to as a virtual array, asshown in FIGS. 11A-C. In some implementations, if two verticallydisplaced transmitters are used which transmit orthogonal signals, itmay be possible to create two vertically displaced virtual array andperform interferometry. One example of vertically displaced transmittersis described in U.S. Pat. No. 8,159,387, entitled “Multi-transmitterInterferometry,” the contents of which are incorporated herein byreference in their entirety.

In some implementations, if M vertically displaced transmitters areused, it is possible to create M virtual arrays. In someimplementations, if two horizontally separated transmitters spaced Lapart are used, it is possible to create two abutting virtual arraysgiving the vehicle an effective array length of L. One example of amulti-transmitter array is described in U.S. Pat. No. 5,295,188 entitled“Synthetic Aperture Side-Looking Sonar Apparatus,” the contents of whichare incorporated herein by reference in their entirety. In someinstances, SAS may use two transmitters placed away from the receivearray to achieve this effective array length. Doubling the array lengthis generally desirable since it doubles the area coverage rate of a SAS.(Since the vehicle moves one effective array length between pings, ifthe array length doubles the range of the sonar doubles. If the robotmaintains its ping rate, it must double its velocity in order to be inposition for the subsequent ping. In either case, the coverage ratedoubles.)

Inventor has recognized that placing a pair of transmitters away fromthe array spaced L apart results in an effective sonar array length of L(which is greater than prior art systems that have an effective arraylength of L/2). The transmitters are typically placed away from thearray because if they are placed on either side on the receive arraythey will not be L apart, but L+D. This spacing results in virtualarrays which may have a missing element, resulting in grating sidelobes.

Inventor's method allows for a more flexible placing of transmitters,allowing for a larger area coverage rate in a smaller package. Themethod includes using multiple transmitters with orthogonal signalsfired non-synchronously and using delays and vehicle translation to formabutting virtual arrays. For a simple array of N elements of size d,with transmitters of size d on either side of the array, the forwardtransmitter will start transmitting first, followed by a delay as thevehicle moves d/2 forward, then the aft transmitter starts firing asshown in FIGS. 11A-C. If, for technical reasons, the transmitters have adifferent spacing, the timing may be adjusted accordingly.

In some implementations, the method allows for a multitude oftransmitters placed along the vehicle. For instance, if fourtransmitters were used spaced L apart (for a total length of 3L), theeffective array length is 2L, and the area coverage rate of the systemquadruples over a baseline SAS. In some implementations, if Mtransmitters are used to lengthen the array, the area coverage increasesto M times the baseline coverage. Likewise, pairs (or larger sets) ofvertically displaced transmitters may be added to create a longerinterferometric array. In the event that the separated along tracktransmitters cannot be placed in the same vertical position, resultingin virtual arrays that are parallel but not collinear, grazing anglecompensation can be used to correct for the vertical displacement.Changing the orthogonal signals used by the transmitters from ping toping may further reduce noise suppression, which may be desirable. Incertain implementations, it may be desirable to fire channels nearlysimultaneously to lengthen the array. In such implementations,projectors may be positioned at 0 (e.g., the fore end of the receiverarray) and L+w (e.g., the aft end of the receiver array), and otherplaces at approximately (k+/−delta)*L where k is an integer and delta issome acceptable variation. Delta may correspond to a delay that is lessthan the time before the bottom bounce.

FIG. 10 depicts a process 1000 for transmitting pulses from a SAS systemhaving such a multiple transmitter arrangement. The process 1000 beginswith proving a transducer array having a receiver array with a pluralityof receiver elements and two transmitter elements (step 1002), eachhaving a width, w. In certain implementations, the transmitting elementsare positioned on either side of the receiver array and along the axisof the receiver array, such as the array depicted in FIGS. 11A-C.Transmitter T1 may be position in the aft position and transmitter T2may be positioned in the fore position such that the vehicle moves in afore direction. The transmitter T2 may ping first (step 1004), afterwhich the vehicle may move fore alongtrack and along the array axisconnecting T1 and T2 (step 1006). The sonar system 100 may determine ifthe sonar array has moved a distance w/2 (step 1008). Once the sonarsystem 100 has moved such a distance, the transmitter T1 may ping, suchthat T1's ping is orthogonal to T2's previous ping (step 1010). Thevehicle may then move fore alongtrack (step 1012), and the sonar system100 may once again query whether the distance traveled is equal to w/2(step 1014). If such a distance has been traversed, the process 1000 maybe repeated from step 1004 and T2 may ping again. If the transmitters ofwidth w were spaced further from the array then the delay would need tobe slightly greater.

They systems and methods described herein may be adapted as desired forboth sonar and radar systems, and accordingly for both syntheticaperture sonar (SAS) and synthetic aperture radar (SAR) systems. Forexample, sonar transducers may be replaced with suitable radartransducers, and one or more components may be modified, added to orremoved from the systems described herein to operate in a sonar andradar regime. In some implementations, the systems and methods may beconfigured to operate as both sonar and radar devices, without departingfrom the scope of the present disclosure. In certain implementations,when the systems and methods are configured for sonar imaging, thefrequencies may be in both high and low frequency ranges in the rangefrom 10 kHz to about 200 kHz. In certain implementations, when thesystems and methods are configured for radar imaging, the frequenciesmay be in the range from 100 MHz to about 30 GHz. Generally, the systemsand methods described herein may be applied for any frequency range,without departing from the scope of the present disclosure.

Overpinging with Multiple SAS Transmitters

In water, the maximum range of a sonar is typically defined as thedistance sound can travel to a target and back before the nexttransmission. However, after the next transmission, the prior pingcontinues to propagate through the water. If it is possible to use aprior ping, then the range of a sonar and/or area coverage rate of asonar may be increased. If N total pings (N−1 prior pings) are fullyused, then the area coverage rate may be increased by a factor of N.

Unfortunately, distant echoes are weaker than closer ones (assumingconstant target strength). By using orthogonal signals, it may bepossible to improve the signal to noise ratio (SNR), but it will stillbe nearly impossible to receive while a transmitter is transmitting. Fora system with a classical range of R, this means it may be (almost)always impossible to observe echoes at ranges just past R.

Accordingly, in yet another aspect, the present disclosure relates to asolution to this SNR problem by adding multiple transmitters that arespaced and fired in a novel order so as provide multiple opportunitiesto recover data from the same range and phase center position and ensurethat at least one of those observations is not jammed. In someimplementations, N separated transmitters may be used to increase therange of a virtual array by N.

In one example, consider an array with length L and maximum classicalSAS range R at a velocity V (V*dt=L/2, R=c/2*dt=cL/4V) with a delaybetween pings of dt=L/2V. Assume that transmitters are (with zero beingon the bow and distance increasing moving aft) at x=0, x=0.25L, x=0.6L.For the second transmitter to form the same virtual array as the firstit must delay firing until the vehicle has moved sufficiently toposition it (corresponding to about 0.25dt). Likewise, if the thirdtransmitter is used, it must be delayed 0.6dt. Suppose ping 1 is formedusing transmitters 1 and 2, ping 2 is formed using transmitters 1 and 3,ping three is formed using transmitters 2 and 3, and then the sequenceis repeated. The firing sequence timing may then be:

Transmitter 1: [0, 1, off, 3, 4, off, . . . ]*dt

Transmitter 2: [0.25, off, 2.25, 3.25, off, 5.25, . . . ]*dt

Transmitter 3: [off, 1.6, 2.6, off, 4.6, 5.6, . . . ]*dt

The combination of all ping times is therefore: [0, 0.25, 1, 1.6, 2.25,2.6, 3, 3.25, 4, 4.6, 5.25, 5.6, etc]*dt. Transmitter 1 and 2 each formvirtual arrays for ping position 1. Since jamming is caused by futurepings, virtual array 1 ping 1 is jammed at the following ranges: [0.25,1, 1.6, 2.25, 2.6, 3, 3.25, 4, 4.6, 5.25, 5.6, etc]*R. Virtual array 2ping 1 is jammed at the following ranges: ([1, 1.6, 2.25, 2.6, 3, 3.25,4, 4.6, 5.25, 5.6, etc]−0.25)*dt=[0.75, 1.35, 2, 2.35, 2.75, 3, 3.75,4.35, 5, 5.35, etc]*R.

Since virtual array 1 is jammed at 0.25 R, but virtual array 2 is not,virtual array 2's signal is used for those immediate ranges. Sincevirtual array 2 is jammed at 0.75 R but virtual array 1 is not, virtualarray 1's signal is used there. Since both arrays are jammed at 2R it isnot possible to get an unjammed observation of 2R, therefore that is thegreatest unjammed range. The ranges for which both arrays are jammed(the ranges at which the fused signal is jammed) are: [3,6,9,12 etc]*R.Likewise, the second ping uses virtual arrays formed by transmitters 1and 3, the third ping uses arrays 2 and 3, etc. Signals are interlacedin a similar matter for latter pings as well. Changing the signals fromping to ping further reduces noise suppression.

Although the transducers/projectors spacing was described above as being0, 0.25*L and 0.6*L, the system 100 may include any number of projectorspositioned at any suitable spacing and having any suitable firingsequences without departing from the scope of the present disclosure.Generally, system 100 may combine overpinging with multiple alongtrackprojectors by either repeating the patter or by generating a newsequence, which may be random. As another example, system 100 mayinclude an array of five projectors positioned at 0, 0.25*L, 0.4142*L,0.6*L and 0.7321*L. Position 0.4142*L corresponds to [sqrt(2)−1]*L andposition 0.732*L corresponds to [sqrt(3)−1]*L. In such an example, thefiring sequences may be:

Transmitter 1: [0, 1, 2, 3, off, 5 etc.] *dt

Transmitter 2: [0.25, 1.25, 2.25, off, 4.25, 5.25, etc.] *dt

Transmitter 3: [0.4142, 1.4142, off, 3.4142, 4.4142, 5.4142, etc.] *dt

Transmitter 4: [0.6, off, 2.6, 3.6, 4.6, 5.6, etc.] *dt

Transmitter 5: [off, 1.7321, 2.7321, 3.7321, 4.7321, off, etc.] *dt

They systems and methods described herein may be adapted as desired forboth sonar and radar systems, and accordingly for both syntheticaperture sonar (SAS) and synthetic aperture radar (SAR) systems. Forexample, sonar transducers may be replaced with suitable radartransducers, and one or more components may be modified, added to orremoved from the systems described herein to operate in a sonar andradar regime. In some implementations, the systems and methods may beconfigured to operate as both sonar and radar devices, without departingfrom the scope of the present disclosure. In certain implementations,when the systems and methods are configured for sonar imaging, thefrequencies may be in both high and low frequency ranges in the rangefrom 10 kHz to about 200 kHz. In certain implementations, when thesystems and methods are configured for radar imaging, the frequenciesmay be in the range from 100 MHz to about 30 GHz. Generally, the systemsand methods described herein may be applied for any frequency range,without departing from the scope of the present disclosure.

Holographic Simultaneous Localization and Mapping (SLAM)

Classical holographic navigation for AUVs, by requiring at least oneimage to be from a synthetic aperture, does not enable a true SLAM(simultaneous localization and mapping) solution. This is because when areal aperture image is correlated with a synthetic aperture image, theposition estimate update is an average one; the estimate can not beisolated to individual states.

In one aspect, the present disclosure relates to a method of removingthe above described requirement of needing at least one syntheticaperture image or quasi-hologram for holographic navigation. In someimplementations, region connecting observation positions are defined andused in such a way as to enable real aperture correlation, e.g., byforming “correlation tubes.” An idealized sonar is introduced andimproves the correlation performance inside the tubes. In someimplementations, a new method of operation allows a typical survey sonarto use correlation tubes to improve its navigation.

As noted earlier, generally, holographic navigation works because ahologram which is defined over a range of angles contains all possibleimages within that set of angles (subject to a few constraints such asfrequency limitations, occlusion, etc). When a real aperture image iscorrelated against a synthetic aperture image, the correlation processtransparently identifies the position in the quasi-hologram where thereal aperture image originated. The correlation works because althoughthe real aperture sonar sees each object from one vantage point, andsees a set of objects from a set of different vantage points, thequasi-hologram is a multi-aspect record. When two real aperture imagesare compared, only targets along a line connecting the two sonars can becorrelated, previously assumed to be impossible. Since the percentage offeatures that are collinear with the two sonars is exceptionally small,and since the correlation result from the area off of that line isnoise, the resulting signal to noise ratio for the correlation is nearzero.

In some implementations, the above described method solves this problemby combining a preliminary navigation estimate, a real aperture array,and “correlation tubes.” In some implementations, if the AUV has areasonably accurate estimate of its position when it made a priorobservation, it can define a reasonably accurate vector connecting thetwo positions. Using a real aperture array, the sonar can steer thesignals received at either position along the direction of that vector.By forming both beams down the correct correlation direction, the signalto noise ratio improves considerably.

In some implementations, the real aperture sonars are long enough thatboth are in the nearfield and it is possible to form a beam withoutspurious information (there is a temptation to call it “noise free”, butit would, of course, still be subject to environmental noise sources).For example, consider two parallel arrays of length L separated by adistance D along the broadside vector of the arrays. If the resolutionof the arrays at the separation distance is less than the length of thearrays then the arrays have sufficient resolution to “block out” energyfrom areas with non-overlapping aspects. (Mathematically, this is verysimilar to a nearfield constraint). Assuming a wavelength λ and making asmall angle assumption, the angular resolution of the arrays is ΔΘ=λ/L,and the across range resolution is Δx=rΔΘ=rλ/L. Since we want Δx<L orrλ/L<L, the technique performs best when r<L²/λ. The nonlinearrelationship between r and L means that the maximum range increasesquickly as the array length grows. For most existing synthetic aperturesonars, with L≈0.50 m and λ≈0.01 m, the maximum range is approximately25 m. In some implementations, with L=2.5 m and λ=0.0083 m the maximumrange is approximately 753 m, or three times the intended survey rangeof 250 m. More generally, given a beam width ϕ the range constraintbecomes r<cos²ϕL²/λ. According to an illustrative implementation, asystem designed according to the beam width constraint can usecorrelation tubes off of broadside without performance degradation.

FIG. 12 depicts a process for simultaneous localization and mapping(SLAM) using real aperture sonar images, according to an illustrativeimplementation of the present disclosure. The sonar system 100 on avehicle may receive a first real aperture acoustic data of a portion ofthe terrain being traversed by the vehicle (step 1202). The sonar system100 (e.g., CCU 160) may receive a first position estimate representing aposition from which the first real aperture image was obtained (step1204). In some instances, the position estimate and the first data (orimage) may have been obtained a priori (either on the same mission or ona prior mission) by the same vehicle as it traverses the terrain. Inother instances, the position estimate and the first data (or image) mayhave been obtained by another vehicle (at the same time or differenttime) traversing the terrain. Both vehicles may be traversing theterrain simultaneously and communicating with each other. The vehiclereceiving the images may be moved to a current position (step 1206). Atthe current position, the vehicle may receive a second position estimaterepresenting the current position (step 1208). The sonar system 100 maythen determine the correlation axis (or correlation tube) connecting thefirst position estimate and the second position estimate (step 1210).The correlation may determine the direction of beamforming for thevehicle in the current position. The sonar system 100 may insonify theterrain and generate a second real aperture image of the terrain fromthe current position (step 1212). The beamformer 134 of the sonar system100 may steer the receiving signal such that the received acousticsignal is directed towards the terrain and along the correlation axis ortube (step 1212), thereby allowing the vehicle at the current positionto view the terrain along the same axis as the direction along which thefirst real aperture image was obtained.

Most synthetic aperture sonars are used to perform surveys. Most surveysfollow a lawnmower pattern. Although it is typical when surveying usinga lawnmower pattern to overlap adjacent passes (to ensure fullcoverage), that overlap is not suitable for holographic navigation(since it has the wrong spectral orientation). Assuming the sonar has amaximum useful range R, swaths are typically spaced 2R apart, and theportion of the imagery from an adjacent survey leg that is orientedcorrectly for holographic navigation exists at the ranges between 2R and3R. The sonar design of the systems herein is advantageous as it enablesclean correlation at triple the baseline operating range.

In some implementations, there may be a plurality of ways to navigaterelative to distant sonar imagery (without looping around to look atit). One such way may include a SAS, such as sonar system 100 andslowing the vehicle down to ⅓ speed, which will then triple the range,enabling a holographic navigation fix. Generally, the vehicle may beslowed down any amount suitable to increase the range by a desirableamount. Another way is to include a dual frequency system, one frequencycan map continuously while another alternates between a normal ping rate(to map) and a ⅓ ping rate (to observe distant regions). A third way maybe to use sequences of orthogonal signals. If the distant echoes aresufficiently orthogonal to the closer echoes they can be used forcorrelation and a navigation update. Unlike imaging at long range, it isnot necessarily to use signals taken at all ranges (including portionswhere the SNR is poor due to other transmissions). It is generallysufficient to use only range regions with “good enough” SNR. Whencorrelating relative to a prior pass it is possible to steer beamsthrough many different prior robot positions, producing many differentmeasurements. Used in post-processing this can result in an accuratemap, although, in some implementations, a limited set of beams may beprocessed in real time.

They systems and methods described herein may be adapted as desired forboth sonar and radar systems, and accordingly for both syntheticaperture sonar (SAS) and synthetic aperture radar (SAR) systems. Forexample, sonar transducers may be replaced with suitable radartransducers, and one or more components may be modified, added to orremoved from the systems described herein to operate in a sonar andradar regime. In some implementations, the systems and methods may beconfigured to operate as both sonar and radar devices, without departingfrom the scope of the present disclosure. In certain implementations,when the systems and methods are configured for sonar imaging, thefrequencies may be in both high and low frequency ranges in the rangefrom 10 kHz to about 200 kHz. In certain implementations, when thesystems and methods are configured for radar imaging, the frequenciesmay be in the range from 100 MHz to about 30 GHz. Generally, the systemsand methods described herein may be applied for any frequency range,without departing from the scope of the present disclosure.

Bistatic and Monostatic Gapfilling for SAS

Synthetic aperture sonars perform poorly in the near “nadir” regime(directly under the vehicle). This is because the partial derivative ofrange with respect to horizontal distance may be approximately zerodirectly beneath the sonar.

There are two traditional solutions to the aforementioned problem. Thefirst is to use a real aperture sonar to image directly under thevehicle. However, as the survey sonar range increases it is generallynecessary to survey from a higher altitude, causing the resolution of areal aperture gapfiller generally to drop for a given aperture. This isdue to two effects: decreased spatial resolution due to increased range,and needing to use lower frequencies/longer wavelengths due to range andabsorption. Even though the aperture can be increased, at realisticaltitudes for a long range survey sonar, it is impossible to getresolutions that approach SAS resolutions.

Applicants solve this problem by using a plurality of sonar vehiclesthat are positioned so as to be able to hear each other's transmissionsand image bistatically. The bistatic image has SAS level resolutionunder either vehicle, and poor resolution in the gaps between thevehicles. By fusing SAS imagery and bistatic SAS imagery it is possibleto have a very high resolution map.

In certain implementations, a long range vehicle may be used inconjunction with a smaller vehicle. In such implementations, the longrange vehicle used for imaging, may include a very large gap beneath it.A second, smaller vehicle may then be used specifically to image thelarge vehicle's gap. The second vehicle may fly the same track line, butbelow the large vehicle.

In certain other implementations, a vehicle may be used at higheraltitude and create gap beneath the vehicle that is comparable to therange of the sonar. In such implementations, one or more adjacentmission legs are then flown such that subsequent legs fill the gap ofprior legs with minimal waste. This design assumes a steeper grazingangle than traditional SAS, but would be considered more typical ofsynthetic aperture radar (SAR). From a grazing anglecompensation/holographic navigation perspective, described above, thereduced range of grazing angles may be beneficial.

Passive Relative Localization

When multiple vehicles perform a survey it is necessary to overlap theirsurvey areas so as to account for inertial navigation errors that accrueover the mission. (This assumes an unknown area without a predeployedbeacon network such as long baseline or ultrashort baseline.) Since theinertial navigation error grows with time, there can be substantialdrift over a long mission. This large drift requires a large overlap,substantially decreasing the net area coverage rate of the sonar. If,instead, vehicles can fly in tight formation then that overlap can bereduced, and gross errors only occur at the edge of the areas imaged bythe formation.

One way for the vehicles to maintain their formation is by using beaconsystems. Using an onboard ultrashort baseline navigation system, it ispossible to measure the range and bearing to another vehicle. However,this requires an additional system.

Instead, Inventor proposes a reduction in which the vehicle (which isassumed to have a synthetic aperture sonar array and system 100)passively listens for the transmissions from other vehicles using itsreal aperture sonar. Using the received signal it may measure thebearing to the other vehicle (but not the range). Measuring rangepassively is difficult since most SAS's are “slaved to speed” meaningthat the pings are timed based on the perceived position of the vehicle.Schemes may be used to passively estimate range based on time ofarrival, but the random component of SAS ping timing makes thisundesirable.

The methods and systems described herein measure range by passivelydithering the vehicles relative to one another and fusing the data in anavigation filter. For instance, consider two vehicles flying inparallel with slowly drifting inertial navigation systems. Assume thatthey each have a base survey velocity of 2 m/s. Assume that for 5minutes vehicle #1 flies at 2.05 m/s and vehicle #2 flies at 1.95 m/s,resulting in a 30 m change in position. If the vehicles are 300 m apart,this corresponds to a 5.6 degree change in position; if they are 310 mapart, this may correspond to an 5.8 degree change. A 2.5 m array with awavelength of 0.008 m has an angular resolution of 0.18 degrees, makinga 10 m range variation observable.

In an alternative instantiation of methods and systems described herein,the vehicle passively listens to a timed pinger on other vehicles toestimate range. A filter onboard the vehicle estimates clock drift.Although dithering is not necessary if listening to a passive pingerdithering does make clock drift more observable.

Bi-Static and Multi-Static Holographic Navigation

In another aspect, the systems and methods described herein relate tolocalizing an emitter or receiver with high precision relative to thesea floor. It can be used as either a fully active system (using wellsynchronized transmitters and receivers) or as a passive system(localizing a transmitter or a receiver based on poorly timed receivedsignals).

The system has many applications. For instance, consider the case oflocalizing a vehicle based on transmissions from a known source. Thisapplies to scenarios such as mine neutralization and submarinenavigation. Bistatic holographic navigation allows small autonomous mineneutralizers to use low frequency signals. Low frequency acousticsignals are more robust to sedimentation than high frequency signals,but low frequency projectors can weigh more than neutralizationplatforms.

Placing the low frequency projector onto another platform (such as alarge diameter unmanned underwater vehicle) allows the mine neutralizerto reduce its sonar to small number of receive elements (which weighvery little). This enables low frequency holographic navigation on avery small platform and, ultimately, a more robust and flexible mineneutralization solution. Similarly, submarines prefer not to use activesonar as it reveals their location, but they have very high qualityreceivers. Bi-static holographic navigation allows submarines tolocalize themselves based on seafloor scattering from a known source(either fixed or moving).

Bi-static holographic navigation can also be used to localize atransmitter using a known receiver. Submarines and stealth aircraftoften possess sonars and radars with electronically scanned phasedarrays that steer energy into narrowly defined areas to reduce theprobability of intercept. Although their outbound beams are narrow, thescattering off the seafloor or ground is less directional. Bi-staticholographic navigation can detect and localize those target based onscattering. Similarly, ships and whales are difficult to accuratelytrack and localize; bistatic holographic navigation can be used to trackthem and reduce collisions (a half dozen blue whales were killed off ofSan Francisco last year).

Bi-static holographic navigation, in its simplest form, may include twoplatforms with accurate synchronized clocks, a known transmit signalwith known timing, position information about the transmitter, and aprior bi-static map. The receive system (either a single channel or anarray) receives the signal, forms an image using timing and geometryinformation, applies grazing angle compensation, correlates the imageagainst a prior image, converts the correlation result to a probabilitydensity function, and uses that probability density function to updateits estimate of the state (position). HF holographic navigationtechniques can be applied to remove various forms of phase error. In amore advanced form, the emitting system (submarine, aircraft, etc) wouldemit a known signal from an unknown position at an unknown time thatwould be received at a known location at a known time. That signal wouldbe back-projected or beamformed, correlated, and used to update aposition estimate.

In a more difficult case, the signal being emitted by the submarine oraircraft would be unknown. In this case, if the direct arrival wereknown, it could be used to form an image. If the signal were unknown, itwould be necessary to learn the signal, use it to form an image, andlocalize the target.

These techniques can also be performed multi-statically and, as always,apply equally to sonar and radar. Although back-scatter is moredesirable, forward scatter can be used. Bi-static holographic navigationfollows a similar process to monostatic holographic navigation. First,it is necessary to create a prior map. Ideally, this is a bi-staticsynthetic aperture image containing all possible frequencies at allpossible angles. In practice, this will likely be band limited and anglelimited. The remaining process depends on whether the transmitter andreceiver are synchronized, which system has a known position, andwhether the signal is known. It is possible, although undesirable, touse two unknown positions.

FIG. 13 shows a bi-static holographic system 1300 including atransmitter 1302 and receivers 1304 and 1314 operating in an underwaterenvironment 1318. The transmitter 1302 may reside on a first platformsuch as an underwater vessel or surface vessel. The receivers 1304 and1314 may reside on second and third platforms that include an AUV,submarine, robot, and the like. FIG. 13 illustrates an acoustic signal1310 directed from transmitter 1302 that results in a scattering signal1312 reflected from a portion 1316 of the underwater terrain 1308. Aportion 1312 a of the scattering signal is directed toward the receiver1304 while another portion 1312 c is directed toward receiver 1314.While FIG. 13 illustrates a system 1300 for navigating an underwaterterrain 1308, other systems may utilize the similar technique fornavigation using radar instead of sonar.

The acoustic transmitter 1302 may be stationary or in motion. Thereceiver 1304 or 1314 may be stationary or in motion. The position ofthe transmitter 1302 may be known by the receivers 1304 and/or 1314. Theposition of the receivers 1304 and/or 1314 may be known by thetransmitter 1302. While FIG. 1 refers to a system 100 capable ofperforming both transmit and receiver functions associated withholographic navigation, one of ordinary skill will readily recognizecertain functions of the system 100 can be implemented on separate atransmitter 1302 and receiver 1304 platforms to enable bi-static ormulti-static holographic navigation. For example, the transmitter 1302may include portions of the sonar unit 110 such as transducer array 112,transmitter 116, and transmitter controller 118. The transmitter 1302may include a central controller 160 or processor that controlsoperations of the transmitter 1302. The transmitter may include a memorythat store certain data such as data in map store 154. The transmitter1302 may include a clock, or the central control unit 160 or navigationcontroller 170 may implement a clock. The transmitter 1302 may includeany other components of system 100 to enable operation of thetransmitter 1302, or of the platform on which the transmitter resides.

One of ordinary skill will also recognize that certain functions of thesystem 100 can be implemented on a separate receiver 1304 or 1314 toenable bi-static or multi-static navigation. For example, the receiver1304 or 1314 may include portions of the sonar unit 110 such astransducer array 112 and receiver 114. The receiver 1304 or 1314 mayinclude a preprocessor 120 including filter conditioner 122 and Dopplercompensator 124. The receiver 1304 or 1314 may include a matched filter130 having a pulse compensator 132 and beamformer 134. The receiver 1304or 1314 may include a signal corrector 104 having a grazing anglecompensator 142 and phase error corrector 144. The receiver 1304 or 1314may include a signal detector 150 having a signal correlator 152 and mapstore 154 or other memory to store navigation-related data. The receiver1304 or 1314 may include a clock, or the central control unit 160 ornavigation controller 170 may implement a clock.

The receiver 1304 or 1314 may include any other components of system 100to enable operation of the receiver 1304 or 1314, or of the platform onwhich the receiver 1304 or 1314 resides. The clock of the transmitter1302 may be synchronized with a clock at the receiver 1304 and/or 1314.Synchronization may be performed prior to launch of the transmitter andreceiver platforms or during operation of the platforms. The platformmay communication information with each other to enable clocksynchronization, update location information, and/or to exchange mapdata. Communications between platforms may be implemented via directacoustic signals, direct wireless link while the platforms are surfaced,or indirect wireless links (e.g., satellite links, ship-to-ship links,and the like).

Depending on whether the transmitter 1302 and receiver 1304 or 1314 aresynchronized, which platform (transmitter or receiver) has a knownposition, and whether the signal is known, the system 1300 is able toestimate the position of either the transmitter 1302 or receivers 1304and 1314. The system 1300 can also estimate the position of either thetransmitter 1302 or receivers 1304 and 1314 when the positions of thetransmitter 1302 or receivers 1304 and 1314 is unknown. FIGS. 14-19 areflow diagrams that illustrate the how the system 1300 estimates thepositions of the receiver 1304 or 1314, or transmitter 1302 depending onthe type of information known by the receiver 1304 or 1314 and/or thetransmitter 1302. An acoustic signal may include one or morecharacteristics or settings such as, without limitation, a noisesequence, linear chirp, encoding, power, frequency, phase, and the like.

FIG. 14 is a flow diagram of a process 1400 for estimating a receiver1304 or 1314 position where timing is known, transmitter position isknown, the acoustic signal 1310 is known, but the receiver 1304 or 1314position is unknown. In this scenario, the receiver 1304 or 1314 has itsclock synchronized with a clock of the transmitter 1302 so that thetiming of the acoustic signal is known by the receiver 1304 or 1314. Thetransmitter 1302 transmits an acoustic signal 1310 through theunderwater environment 1318 toward the portion 1316 of the underwaterterrain 1308. The acoustic signal 1310 is reflected or scattered fromthe portion 1316 of the underwater terrain 1308 in multiple directions.A portion 1312 a of the scattered signal is received by the receiver1304 (block 1402). Again, the receiver may be an AUV or submarine. Thereceiver 1304 applies the received signal 1312 a to a matched filter 130(block 1404). The receiver 1304 may also apply the signal to apreprocessor 120 prior to the matched filter 130. The receiver forms animage using, for example, beamformer 134 (block 1406).

Once an image is formed, the receiver applies bi-static grazing anglecompensation using grazing angle compensator 142 (block 1408). The imagemay also be phase error corrected by a phase error corrector 144 whenhigh frequency holographic navigation is used. The receiver 1304 thencorrelates the image with a prior map as discussed previously hereinwith respect to holographic navigation (1410). The map may include amonostatic or bi-static map. Over modest angles, a monostatic prior mapcan be a substitute for a bistatic prior map. The receiver 1304 may usea processor such as central control unit 160 or a dedicated processor toconvert the correlation result to a probability density function (block1412). The receiver 1304, using a processor such as central control unit160, then updates the state estimate (e.g., location) of receiver 1304(block 1414). The processor 160 may also determine the heading ordirection of motion of the receiver 1304 and/or platform on which thereceiver 1304 resides.

In certain implementations, the real image is taken and a sum of theenvelopes of small image correlation regions with approximatelystationary phase is calculated before calculating a probability densityfunction based on that sum. This may be similar to a speckle reductiontechnique used in imaging methods. The sums can either be for a singlealtitude solution or for multiple altitude solutions; if multiplealtitude solutions are used then the technique measures altitude bias.In certain implementations, using the envelope only (the absolute valueof the correlation result) removes the relative phase differencesbetween correlation results. It is important to note that summingtogether a large number of correlation images results in a transitionfrom Rayleigh to Gaussian distributed speckle intensity. The receiver1304 may account for this difference when converting the correlationresult to a probability density function. Previous holographicnavigation techniques, which use the Rayleigh distribution, typicallyfail when presented with correlation results based on sub-imagesummation. By switching to a more representative distribution, thereceiver 1304 avoids this deficiency. When a small number of images aresummed together the distribution may not yet be fully Gaussian. Hence,the receiver 1304 may be better represented the distribution using someother distribution such as a K-distribution.

FIG. 15 is a flow diagram of a process 1500 for estimating a receiver1304 or 1314 position where timing is unknown, transmitter 1302 positionis known, the acoustic signal 1310 is known and the receiver 1304 or1314 position is unknown. In this scenario, the timing of the acousticsignal is unknown by the receiver 1304 or 1314. The transmitter 1302transmits an acoustic signal 1310 through the underwater environment1318 toward the portion 1316 of the underwater terrain 1308. Theacoustic signal 1310 is reflected or scattered from the portion 1316 ofthe underwater terrain 1308 in multiple directions. A portion 1312 a ofthe scattered signal is received by the receiver 1304 (block 1502). Thereceiver 1304 applies the received signal 1312 a to a matched filter 130(block 1504). The receiver 1304 may also apply the signal to apreprocessor 120 prior to the matched filter 130. The receiver formsmultiple images for hypothesis testing using, for example, beamformer134 (block 1506).

Once the multiple images are formed, the receiver applies bi-staticgrazing angle compensation using grazing angle compensator 142 (block1508). The images may also be phase error corrected by a phase errorcorrector 144 when high frequency holographic navigation is used. Thereceiver 1304 then correlates the images with a prior map as discussedpreviously herein with respect to holographic navigation (block 1510).The receiver 1304 may use a processor such as central control unit 160or a dedicated processor to convert the correlation result to aprobability density function (block 1512). The receiver 1304, using aprocessor such as central control unit 160, determines which of themultiple images is most accurate based on hypothesis testing and thenupdates the state estimate (e.g., location) of receiver 1304 (block1514). Hypothesis testing may include a processing in which the receiver1304 makes crude estimate of the transmitter 1302 location and thencreates different images based on where the transmitter could belocated. The processor 160 may also determine the heading or directionof motion of the receiver 1304 and/or platform on which the receiver1304 resides.

FIG. 16 is a flow diagram of a process 1600 for estimating a receiver1304 position where timing is unknown, transmitter 1302 position isknown, the acoustic signal 1310 is unknown and the receiver 1304position is unknown. In this scenario, the transmitter 1302 transmits anacoustic signal 1310 through the underwater environment 1318 toward theportion 1316 of the underwater terrain 1308. The acoustic signal 1310 isreflected or scattered from the portion 1316 of the underwater terrain1308 in multiple directions. A portion 1312 a of the scattered signal isreceived by the receiver 1304 (block 1602). The receiver 1304 learns theacoustic signal 1310 based on detecting one or more of the portions ofthe scattered signal 1312 and other known data such as the transmitter1302 position (block 1604). The receiver 1304 may employ any number oflearning techniques such as, without limitation, a technique including asimulated annealing algorithm and/or process to learn the acousticsignal 1310. The receiver 1304 applies the received signal 1312 a to amatched filter 130 (block 1606). The receiver 1304 may also apply thesignal to a preprocessor 120 prior to the matched filter 130. Thereceiver forms an image using, for example, beamformer 134 (block 1608).

Once an image is formed, the receiver applies bi-static grazing anglecompensation using grazing angle compensator 142 (block 1610). The imagemay also be phase error corrected by a phase error corrector 144 whenhigh frequency holographic navigation is used. The receiver 1304 thencorrelates the image with a prior map as discussed previously hereinwith respect to holographic navigation (1612). The receiver 1304 may usea processor such as central control unit 160 or a dedicated processor toconvert the correlation result to a probability density function (block1614). The receiver 1304, using a processor such as central control unit160, then updates the state estimate (e.g., location) of receiver 1304(block 1616). The processor 160 may also determine the heading ordirection of motion of the receiver 1304 and/or platform on which thereceiver 1304 resides.

FIG. 17 is a flow diagram of a process 1700 for estimating a transmitter1302 position where timing is known, transmitter 1302 position isunknown, the acoustic signal 1310 is known and the receiver 1304position is known. In this scenario, the receiver 1304 or 1314 has itsclock synchronized with a clock of the transmitter 1302 so that thetiming of the acoustic signal 1310 is known by the receiver 1304 or1314. The transmitter 1302 transmits an acoustic signal 1310 through theunderwater environment 1318 toward the portion 1316 of the underwaterterrain 1308. The acoustic signal 1310 is reflected or scattered fromthe portion 1316 of the underwater terrain 1308 in multiple directions.A portion 1312 a of the scattered signal is received by the receiver1304 (block 1702). Again, the receiver may be an AUV or submarine. Thereceiver 1304 applies the received signal 1312 a to a matched filter 130(block 1704). The receiver 1304 may also apply the signal to apreprocessor 120 prior to the matched filter 130. The receiver forms animage using, for example, beamformer 134 (block 1706).

Once an image is formed, the receiver applies bi-static grazing anglecompensation using grazing angle compensator 142 (block 1708). The imagemay also be phase error corrected by a phase error corrector 144 whenhigh frequency holographic navigation is used. The receiver 1304 thencorrelates the image with a prior map as discussed previously hereinwith respect to holographic navigation (block 1710). The receiver 1304may use a processor such as central control unit 160 or a dedicatedprocessor to convert the correlation result to a probability densityfunction (block 1412). The receiver 1304, using a processor such ascentral control unit 160, then updates the state estimate (e.g.,location) of transmitter 1302 (block 1714). The processor 160 may alsodetermine the heading or direction of motion of the transmitter 1302and/or platform on which the transmitter 1302 resides.

FIG. 18 is a flow diagram of a process 1800 for estimating a transmitter1302 position where timing is unknown, transmitter 1302 position isunknown, the acoustic signal 1301 is known and the receiver 1314position is known. In this scenario, the timing of the acoustic signal1310 is unknown by the receiver 1304 or 1314. The transmitter 1302transmits an acoustic signal 1310 through the underwater environment1318 toward the portion 1316 of the underwater terrain 1308. Theacoustic signal 1310 is reflected or scattered from the portion 1316 ofthe underwater terrain 1308 in multiple directions. A portion 1312 a ofthe scattered signal is received by the receiver 1304 (block 1802). Thereceiver 1304 applies the received signal 1312 a to a matched filter 130(block 1804). The receiver 1304 may also apply the signal to apreprocessor 120 prior to the matched filter 130. The receiver formsmultiple images for hypothesis testing using, for example, beamformer134 (block 1806).

Once the multiple images are formed, the receiver applies bi-staticgrazing angle compensation using grazing angle compensator 142 (block1808). The images may also be phase error corrected by a phase errorcorrector 144 when high frequency holographic navigation is used. Thereceiver 1304 then correlates the images with a prior map as discussedpreviously herein with respect to holographic navigation (block 1810).The receiver 1304 may use a processor such as central control unit 160or a dedicated processor to convert the correlation result to aprobability density function (block 1812). The receiver 1304, using aprocessor such as central control unit 160, determines which of themultiple images is most accurate based on hypothesis testing and thenupdates the state estimate (e.g., location) of transmitter 1302 (block1814). The processor 160 may also determine the heading or direction ofmotion of the transmitter 1302 and/or platform on which the transmitter1302 resides.

FIG. 19 is a flow diagram of a process 1900 for estimating a transmitter1302 position where timing is unknown, transmitter 1302 position isunknown, the acoustic signal 1310 is unknown, but the receiver position1304 or 1314 is known. In this scenario, the transmitter 1302 transmitsan acoustic signal 1310 through the underwater environment 1318 towardthe portion 1316 of the underwater terrain 1308. The acoustic signal1310 is reflected or scattered from the portion 1316 of the underwaterterrain 1308 in multiple directions. A portion 1312 a of the scatteredsignal is received by the receiver 1304 (block 1902). The receiver 1304learns the acoustic signal 1310 based on detecting one or more of theportions of the scattered signal 1312 and other known data such as thetransmitter 1302 position (block 1904). The receiver 1304 applies thereceived signal 1312 a to a matched filter 130 (block 1906). Thereceiver 1304 may also apply the signal to a preprocessor 120 prior tothe matched filter 130. The receiver forms an image using, for example,beamformer 134 (block 1908).

Once an image is formed, the receiver 1304 applies bi-static grazingangle compensation using grazing angle compensator 142 (block 1910). Theimage may also be phase error corrected by a phase error corrector 144when high frequency holographic navigation is used. The receiver 1304then correlates the image with a prior map as discussed previouslyherein with respect to holographic navigation (block 1912). The receiver1304 may use a processor such as central control unit 160 or a dedicatedprocessor to convert the correlation result to a probability densityfunction (block 1914). The receiver 1304, using a processor such ascentral control unit 160, then updates the state estimate (e.g.,location) of transmitter 1302 (block 1916). The processor 160 may alsodetermine the heading or direction of motion of the transmitter 1302and/or platform on which the transmitter 1302 resides.

The systems and methods described herein may be realized as a softwarecomponent operating on a conventional data processing system such as aUnix system. In that implementation, these mechanisms can be implementedas a C language computer program, or a computer program written in anyhigh level language including Matlab, C++, Fortran, Java or BASIC.Additionally, in an implementation where microcontrollers or DSPs areemployed, the mapping mechanism can be realized as a computer programwritten in microcode or written in a high level language and compileddown to microcode that can be executed on the platform employed. Thedevelopment of such data processing systems is known to those of skillin the art, and such techniques are set forth in Digital SignalProcessing Applications with the TMS320 Family, Volumes I, II, and III,Texas Instruments (1990). Additionally, general techniques for highlevel programming are known, and set forth in, for example, Stephen G.Kochan, Programming in C, Hayden Publishing (1983). It is noted thatDSPs are particularly suited for implementing signal processingfunctions, including preprocessing functions such as image enhancementthrough adjustments in contrast, edge definition and brightness.Developing code for the DSP and microcontroller systems follows fromprinciples well known in the art. The system also provides and enablesas is known to those of skill in the art, object oriented frameworks aregenerally understood as a set of classes that embody a design forsolutions to a family of related problems. See The C++ ProgrammingLanguage, 2nd Ed., Stroustrup Addision-Wesley. Accordingly, a frameworkfor mapping and filtering may be created that provides a prefabricatedstructure, or template, of a working mapping and filtering program.

Variations, modifications, and other implementations of what isdescribed may be employed without departing from the spirit and scope ofthe systems and methods described herein. For example, though thesystems and methods are described in the context of underwater mappingand navigation using sonar signals, the systems and methods may beequally applicable for mapping and navigating in aerial or other land orspace-based terrains and using other imaging technologies include radar,optical signals, and any acoustic or electromagnetic signal. Moreover,any of the method and system features described above or incorporated byreference may be combined with any other suitable method or systemfeature disclosed herein or incorporated by reference, and is within thescope of the contemplated systems and methods. The systems and methodsmay be embodied in other specific forms without departing from thespirit or essential characteristics thereof. The foregoingimplementations are therefore to be considered in all respectsillustrative, rather than limiting of the systems and methods describedherein.

What is claimed is:
 1. An underwater relative orientation systemcomprising: a first blazed array with elements arranged in a firstdirection; a second blazed array with elements arranged in a seconddirection, wherein the second direction is substantially orthogonal tothe first direction, wherein the first blazed array and second blazedarray each emit sonic output for receipt by an underwater vehicle, suchthat the underwater vehicle can orient itself relative to the underwaterrelative orientation system.
 2. The system of claim 1, wherein theunderwater vehicle includes an autonomous underwater vehicle (AUV). 3.The system of claim 1, wherein a first frequency range of the firstblazed array and a second frequency range of the second blazed arrayeach include a minimum frequency greater than 100 kHz.
 4. The system ofclaim 3, wherein the first frequency range is 300-600 kHz and the secondfrequency range is 600-1200 kHz.
 5. The system of claim 1, furthercomprising a passive phased array to determine an orientation of theunderwater vehicle with respect to the relative orientation system. 6.The system of claim 1, wherein the first and second blazed arraystransmit different frequencies at different angles and the first andsecond blazed array create a two dimensional grid of frequencies.
 7. Amethod for docking an underwater vehicle to an underwater dockingsystem, comprising: receiving a first sonic output from a first blazedarray with elements arranged in a first direction; receiving a secondsonic output from a second blazed array with elements arranged in asecond direction; determining a first position of the first sonic outputwithin a first frequency range; determining a second position of thesecond sonic output within a second frequency range; based on the firstand second positions, updating a path of the underwater vehicle suchthat the underwater vehicle is centered horizontally and vertically withrespect to the underwater docking system.
 8. The method of claim 7,wherein updating the path of the underwater vehicle further comprises:comparing the first position of the first sonic output relative to amidpoint of the first frequency range; based on the comparison,adjusting the path of the underwater vehicle in the first direction. 9.The method of claim 8, wherein updating the path of the underwatervehicle further comprises: comparing the second position of the secondsonic output relative to a midpoint of the second frequency range; basedon the comparison, adjusting the path of the underwater vehicle in thesecond direction.
 10. The method of claim 7, wherein the first andsecond blazed arrays transmit different frequencies at different anglesand the first and second blazed array create a two dimensional grid offrequencies.
 11. The method of claim 7, wherein the first and secondfrequency ranges include a minimum frequency greater than 100 kHz. 12.The method of claim 11, wherein the first frequency range is 300-600 kHzand the second frequency range is 600-1200 kHz.
 13. The method of claim7, further comprising using a passive phase array to determine anorientation of the underwater vehicle with respect to the underwaterdocking system.
 14. The method of claim 13, wherein using the passivephase array further comprises receiving a sonic output from the passivephase array; based on the sonic output, determining a position of afront of the underwater vehicle relative to the underwater dockingsystem; and adjusting the position of the front of the underwatervehicle such that the front of the underwater vehicle faces theunderwater docking system.
 15. The method of claim 7, wherein theunderwater vehicle includes an autonomous underwater vehicle (AUV). 16.The method of claim 7, wherein the first blazed array and the secondblazed array are located on the underwater docking system.
 17. Anunderwater vehicle comprising: a motor controller for steering thevehicle; a central control unit for controlling movement of the vehicle;and a passive sonar for receiving sonic output from an underwaterdocking system, wherein the sonic output includes a first blazed arrayand a second blazed array substantially orthogonal to the first blazedarray, and wherein based on the sonic output, the central control unitinstructs the motor controller to steer the underwater vehicle such thatthe underwater vehicle docks to the underwater docking system.
 18. Theunderwater vehicle of claim 17, wherein the received sonic output formsa two dimensional grid of frequencies.